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Effective monitoring of structural and biological systems is an extremely important research area that enables technology development for future intelligent devices, platforms, and systems. This presentation provides an overview of research efforts funded by the Office of Naval Research (ONR) to establish structural health monitoring (SHM) methodologies in the human domain. Basic science efforts are needed to utilize SHM sensing, data analysis, modeling, and algorithms to obtain the relevant physiological and biological information for human-specific health and performance conditions. This overview of current research efforts is based on the Monitoring Osseointegrated Prosthesis (MOIP) program. MOIP develops implantable and intelligent prosthetics that are directly anchored to the bone of residual limbs. Through real-time monitoring, sensing, and responding to osseointegration of bones and implants as well as interface conditions and environment, our research program aims to obtain individualized actionable information for implant failure identification, load estimation, infection mitigation and treatment, as well as healing assessment. Looking ahead to achieve ultimate goals of SHM, we seek to expand our research areas to cover monitoring human, biological and engineered systems, as well as human-machine interfaces. Examples of such include 1) brainwave monitoring and neurological control, 2) detecting and evaluating brain injuries, 3) monitoring and maximizing human-technological object teaming, and 4) closed-loop setups in which actions can be triggered automatically based on sensors, actuators, and data signatures. Finally, some ongoing and future collaborations across different disciplines for the development of knowledge automation and intelligent systems will be discussed.

We investigate the nonlinear wave caused by interaction of surfaces of a fatigue crack, and study the effect of the crack’s contact compression on the magnitudes of nonlinear waves. Nonlinear wave modulation is generated when two ultrasonic waves having different frequencies passing through a crack, and the so-called nonlinear ultrasonic wave modulation technique is developed using this nonlinear waves. However, the magnitude of the nonlinear wave decreases as the crack contact compression increases because the large compression prevents the cracks from opening in motion. Even if the nonlinear wave modulation occurs in the damaged structures under compression, the magnitude might be different with the magnitude without compression. Consequently, finding the range of contact compression and the excitation directions with which the nonlinear wave modulation might occur is essential to use the technique for structural components under constraint compression. In order to examine the relations between the constraint compression and nonlinear wave modulation, we conduct numerical simulations under various compression by changing the excitation directions. The numerical model consists of a thin aluminum plate with a fatigue crack under constant compression, and the crack surfaces are modeled mimicking the shapes of real cracks. The roughness of the crack is determined using the crack widths obtained from optical measurement of fatigue cracks. Effective range of contact compression to generate nonlinear wave and the requirements to make the magnitudes of nonlinear waves non-trivial are described.

Composite material use in aerospace structures has grown over the last two decades and more recently there has been an increase in the use of anisotropic composite layups. One of the most promising SHM techniques is Acoustic Emission (AE) using Lamb waves. Previous location algorithms, capable of locating damage such as cracks, delamination and debonding, have focused their application to either isotropic or quasi-isotropic structures. Previous work was dedicated to anisotropic structures based on single Lamb wave mode propagations.

The scope of this work is to include different modes in the AE location algorithm to improve its location. There are cases where it is likely that different modes trigger different transducers for the same event. The transducer time-of-flight is dependent on the mode velocity, therefore an AE location calculated from single-modal algorithm would expect to have significant location inaccuracy. By considering the possibility of different Lamb wave modes triggering each sensor in the location algorithm, and using certain mathematical and physical assumptions, significant improvements of the AE location can be reached, reducing NDT burden.

The multi-modal algorithm also includes the ability to locate AE in anisotropic material based on previous proven single-modal algorithm known as Elliptical algorithm. Such a multi-modal elliptical approach taken in the algorithm discussed in the work is expected to reduce significantly the AE location error for highly anisotropic material. Based on analytical equations, this algorithm processes large amounts of AE data in a condensed period of time, allowing live structural monitoring of large assets.

Lamb wave dispersion curves for isotropic plates are extracted from measured sensor data by matrix pencil (MP) method. A piezoelectric wafer emits a linear chirp signal as broadband excitation to generate Lamb waves in isotropic plates. The propagating waves are measured at discrete locations along a wave ray direction with a sensor 1-D laser Doppler vibrometer (LDV). The out-of-plane velocities are first Fourier transformed into either space-frequency x-ω domain or wavenumber-time k-t domain. The matrix pencil method is then employed to extract the dispersion curves for various wave modes simultaneously. In addition, the phase and group velocity dispersion curves are deduced by the relation between wavenumber and frequency. In this research, the inspections for dispersion relations on isotropic plates are demonstrated and compared by two-dimensional Fourier transform (2D-FFT) and MP method. The results are confirmed by theoretical curves computed numerically. It has demonstrated that the MP method is robust in recognining/differentiating different wave modes, including higher order ones.

Fiber Bragg grating (FBG) sensors are excellent transducers for ultrasonic signal detection in structural health monitoring (SHM) application. While the FBG sensors are typically bonded directly on the surface of a structure to collect signals, one of the major challenges arises from demodulating relevant information from the low amplitude signal. The authors have experimentally demonstrated that the ultrasonic wave detection sensitivity of FBG sensors can be increased by bonding optical fiber away from the FBG location. This configuration is referred to here as remote bonding. However the mechanism causing this phenomenon has not been explored. In this work, we simulate the previous experimental work through a transient analysis based on the finite element method, and the output FBG response is calculated through the transfer matrix method. We first model an optical fiber bonded on the surface of an aluminum plate with an adhesive. The consistent input signal is excited to the plate, which is detected by the directly and remotely-bonded FBGs. The effect of the presence of the adhesive around the FBG is investigated by analyzing strain and displacement along the length of the FBGs at the locations of direct and remote bonding cases, and the consequent output FBG responses. The result demonstrates that the sensitivity difference between the direct and remote bonding cases is originated from shear lag effect due to adhesive.

A two-stage algorithm that detects and locates damages in thin walled structures using Lamb wave signals is proposed. Isotropic plate and shell structures with adhesively bonded piezoelectric transducers in circular and rectangular array patterns are considered. Lamb waves are generated and sensed by these transducers in pitch-catch mode, before and after making damages in the structure-under-test for baseline subtraction. In the damage identification process, first the correlation coefficient is determined using current and baseline signals. Further, the Trilateration method is adopted to locate the damage using parameters like Time-of-Flight and Group velocity from the damage-scattered Lamb wave signals.

The potential return of samples back to Earth in a future NASA mission would require protection of our planet from the risk of bringing uncontrolled biological materials back with the samples. In order to ensure this does not happen, it would be necessary to “break the chain of contact (BTC)”, where any material reaching Earth would have to be inside a container that is sealed with extremely high confidence. Therefore, it would be necessary to contain the acquired samples and destroy any potential biological materials that may contaminate the external surface of their container while protecting the sample itself for further analysis. A novel synchronous separation, seaming, sealing and sterilization (S4) process for sample containerization and planetary protection has been conceived and demonstrated. A prototype double wall container with inner and outer shells and Earth clean interstitial space was used for this demonstration. In a potential future mission, the double wall container would be split into two halves and prepared on Earth, while the potential on-orbit execution would consist of inserting the sample into one of the halves and then mating to the other half and brazing. The use of brazing material that melts at temperatures higher than 500°C would assure sterilization of the exposed areas since all carbon bonds are broken at this temperature. The process would be executed in two-steps, Step-1: the double wall container halves would be fabricated and brazed on Earth; and Step-2: the containerization and sterilization process would be executed on-orbit. To prevent potential jamming during the process of mating the two halves of the double wall container and the extraction of the brazed inner container, a cone-within-cone approach has been conceived and demonstrated. The results of this study will be described and discussed.

A large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is able to monitor strain changes over a mesoscale structural surface and endure large deformations without being damaged under cracking. Previous tests verified that the SEC is able to detect, localize, and monitor fatigue crack activities under low-cycle fatigue loading. In this paper, to examine the SEC’s capability of monitoring high-cycle fatigue cracks, a compact specimen is tested under cyclic tension, designed to ensure realistic crack opening sizes representative of those in real steel bridges. To overcome the difficulty of low signal amplitude and relatively high noise level under high-cycle fatigue loading, a robust signal processing method is proposed to convert the measured capacitance time history from the SEC sensor to power spectral densities (PSD) in the frequency domain, such that signal’s peak-to-peak amplitude can be extracted at the dominant loading frequency. A crack damage indicator is proposed as the ratio between the square root of the amplitude of PSD and load range. Results show that the crack damage indicator offers consistent indication of crack growth.

Development of an electronic skin with ultra-high pressure sensitivity is now of critical importance due its broad range of applications including prosthetic skins and biomimetic robotics. Microstructured conductive composite elastomers can acquire mechanical and electrical properties analogous to those of natural skin. One of the most prominent features of human skin is its tactile sensing property which can be mimicked in an electronic skin. Herein, an electrically conductive composite comprising polydimethylsiloxane and conductive fillers is used as a flexible and stretchable piezoresistive sensor. The electrical conductivity is induced within the elastomer matrix via carbon nanotubes whereas the piezoresistivity is obtained by means of microstructuring the surface of the substrate. An interlocked array of pyramids in micro-scale allows the change in the contact resistance between two thin layers of the composite upon application of an external load. Deformation of the interlocked arrays endows the sensor with an ultra-high sensitivity to the external pressures within the range of human skin perception. Moreover, using finite element analysis, the change in the contact are between the two layers was captured for different geometries. The structure of the sensor can be optimized through an optimization model in order to acquire maximum sensitivity.

As the desire for wearable electronics increases and the soft robotics industry advances, the need for novel sensing materials has also increased. Recently, there have been many attempts at producing novel materials, which exhibit piezoresistive behavior. However, one of the major shortcomings in strain sensing technologies is in the fabrication of such sensors. While there is significant research and literature covering the various methods for developing piezoresistive materials, fabricating complex sensor platforms is still a manufacturing challenge.

Here, we report a facile method to fabricate multidirectional embedded strain sensors using additive manufacturing technology. Pure thermoplastic polyurethane (TPU) and TPU/multiwall carbon nanotubes (MWCNT) nanocomposites were 3D printed in tandem using a low-cost multi-material FDM printer to fabricate uniaxial and biaxial strain sensors with conductive paths embedded within the insulative TPU platform. The sensors were then subjected to a series of cyclic strain loads. The results revealed excellent piezoresistive responses of the sensors with cyclic repeatability in both the axial and transverse directions and in response to strains as high as 50%. Further, while strain-softening did occur in the embedded printed strain sensors, it was predictable and similar to the results found in the literature for bulk polymer nanocomposites. This works demonstrates the possibility of manufacturing embedded and multidirectional flexible strain sensors using an inexpensive and versatile method, with potential applications in soft robotics and flexible electronics and health monitoring.

Piezoelectric materials such as polyvinylidene fluoride (PVDF) or lead zirconate titanate (PZT) are the fundamental materials for fabricating piezoelectric based energy harvesters. However, the drawbacks for these two materials are: (i) poor mechanical properties of PZT (e.g., brittle, low fracture stress); (ii) low energy efficiency of PVDF. Extensive research work has been made on investigating the piezoelectric property of PVDF. But very few attentions have been paid on mechanical property. In this paper, we report a synthetization of PVDF using a very low multi-wall carbon nanotubes (MWCNT) fraction in PVDF to enhance both the mechanical property and piezoelectricity of PVDF/MWCNT composite. Through a series of well-controlled experiments, we found that the adding of MWCNT in PVDF affects the crystalline structure. Our experimental results also showed that the maximum tensile stress and maximum piezoelectric voltage constant both occurs at PVDF/MWCNT (0.05wt%). However, with the MWCNT fraction exceeded 0.05wt%, the maximum tensile stress and maxi- mum piezoelectric voltage constant decreased.

Aerospace structural systems are prone to structural damage during their use by vibration, impact, material degradation, and other factors. Due to the harsh environments in which aerospace structures operate, aerospace structures are susceptible to various types of damage and often their structural integrity is jeopardized unless damage onset is detected in timely manner. Yet, current state-of-the-art sensor technologies are still limited for structural health monitoring (SHM) of aerospace structures due to their high power consumption, need for large form factor design, and manageable integration into aerospace structures. This study proposes a design of multilayered self-powered strain sensor by coupling mechano-luminescent (ML) property of copper-doped zinc sulfide (ZnS:Cu) and mechano-optoelectronic (MO) property of poly(3-hexylthiophene) (P3HT). One functional layer of the self-powered strain sensor is ZnS:Cu-based elastomeric composites that emit light in response to mechanical deformation. Another functional layer is P3HT-based thin films that generate direct current (DC) under light illumination and DC magnitude changes with applied strain. First, ML light emission characteristics of ZnS:Cu-based composites are studied under cyclic tensile strain with two various maximum strain up to 10% and 15% at various loading frequencies from 5 Hz to 20 Hz. Second, piezo-optical properties of P3HT-based thin films are investigated by acquiring light absorption of the thin films at various strains from 0% to 2% tensile strain. Last, micro-mechanical properties of the P3HT-based thin films are characterized using nanoindentation.

This paper demonstrates improvements on semiconductor nanomembrane based high frequency pressure sensors that utilize silicon on insulator techniques in combination with nanocomposite materials. The low-modulus, conformal nanomembrane sensor skins with integrated interconnect elements and electronic devices could be applied to vehicles or wind tunnel models for full spectrum pressure analysis. Experimental data demonstrates that: 1) silicon nanomembrane may be used as single pressure sensor transducers and elements in sensor arrays, 2) the arrays may be instrumented to map pressure over the surfaces of test articles over a range of Reynolds numbers, temperature and other environmental conditions, 3) in the high frequency range, the sensor is comparable to the commercial high frequency sensor, and 4) in the low frequency range, the sensor is much better than the commercial sensor. This supports the claim that nanomembrane pressure sensors may be used for wide bandwidth flow analysis.

In this study, the representative data extraction is performed for the shape information model of civil infrastructures. The scan data is extracted by octree data processing. The octree data processing generates a voxel of 3D space which is recursively subdivided into eight sub-voxels. The point cloud of the scan data was converted to voxels and sampled. The experimental site for terrestrial laser scanning is located at Sungkyunkwan University. The scanned structure is a steel girder bridge. For Terrestrial Laser Scanning(TLS), the Leica ScanStation C10 was used. The scan data was condensed 92% and the octree model was constructed with a 2 millimeter resolution. Accuracy verification was carried out in order to confirm that the data characteristic were retained. The objective of this study is to use Octree data processing to reduce large amounts of point clouds. Octree data processing will be the foundation for shape information model of the large structures such as double-deck tunnels, buildings and bridges. The research will be expected to improve the efficiency of shape information model.

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters’ centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

This paper outlines and discusses a few associated details of a smart cities approach to the mapping and condition assessment of urban underground infrastructure. Underground utilities are critical infrastructure for all modern cities. They carry drinking water, storm water, sewage, natural gas, electric power, telecommunications, steam, etc. In most cities, the underground infrastructure reflects the growth and history of the city. Many components are aging, in unknown locations with congested configurations, and in unknown condition. The technique uses sensing and information technology to determine the state of infrastructure and provide it in an appropriate, timely and secure format for managers, planners and users. The sensors include ground penetrating radar and buried sensors for persistent sensing of localized conditions. Signal processing and pattern recognition techniques convert the data in information-laden databases for use in analytics, graphical presentations, metering and planning. The presented data are from construction of the St. Paul St. CCTA Bus Station Project in Burlington, VT; utility replacement sites in Winooski, VT; and laboratory tests of smart phone position registration and magnetic signaling. The soil conditions encountered are favorable for GPR sensing and make it possible to locate buried pipes and soil layers. The present state of the art is that the data collection and processing procedures are manual and somewhat tedious, but that solutions for automating these procedures appear to be viable. Magnetic signaling with moving permanent magnets has the potential for sending lowfrequency telemetry signals through soils that are largely impenetrable by other electromagnetic waves.

In this study, the recursive update algorithm of subspace system identification is proposed for estimating dynamic characteristics of a time-varying multi-input multi-output (MIMO) building structure using the pastinput/ output multivariable output-error state space (PO-MOESP) based algorithm. First, the recursive subspace identification with Bona-fide LQ renewing algorithm (RSI-BonaFide-Oblique) incorporated with moving window technique is utilized to identify modal parameters such as natural frequencies, damping ratios and mode shapes at each time instant during the strong earthquake excitation, which assumes the equivalent linear dynamic characteristic in every moving time window with a fixed length. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, several damage detection algorithms related to the reconstructed system stiffness matrix using simplified model was derived from the previously identified modal parameters to quantify the damage extent, specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction. To demonstrate the capability of the proposed recursive subspace identification algorithm and the damage assessment technique, dynamic response data generated from a shaking table test of a two 3-story steel structure experiment is used to verify the algorithms on detecting damage caused by nonlinear behavior occurring on the steel column. Besides, a building seismic response data (collected from Chi-Chi earthquake) was also used to evaluate the damage situation during severe earthquake excitation.

In 2013, one in every nine steel bridges in North America was deemed to be in need of repair, retrofit or replacement. There are an estimated 200,000 steel bridges in the US alone, and an estimated $76 billion is required to return all bridges back to adequate service levels. As a result, monitoring is crucial to ensure the longevity of these structures and to avoid unnecessary replacement. To evaluate the use of distributed fiber optic strain sensors to monitor truss bridges, laboratory tests were undertaken. A scale model of the Mile 17.7 CN Rail Bridge in Jordan, Ontario, Canada was constructed and instrumented with fiber optic strain sensors prior to testing. The experimental program consisted of three point loading under three different connection conditions: i) all bolts were present and torqued, ii) one of two bolts in the connection was removed, and iii) the remaining bolts were loosened. The distributed strain measurements were used to calculate both the axial and bending strains along the full length of each member, along with the variation in truss behavior as a result of the change in connection conditions. The results indicate that the connection conditions at the truss joints can alter the behavior of the bridge.

Development of fatigue cracking is affecting the structural performance of many of welded steel bridges in the United States. This paper presents a support vector machine (SVM) method for the detection of distortion-induced fatigue cracking in steel bridge girders based on the data provided by self-powered wireless sensors (SWS). The sensors have a series of memory gates that can cumulatively record the duration of the applied strain at a specific threshold level. Each sensor output has been characterized by a Gaussian cumulative density function. For the analysis, extensive finite element simulations were carried out to obtain the structural response of an existing highway steel bridge girder (I-96/M- 52) in Webberville, Michigan. The damage states were defined based on the length of the crack. Initial damage indicator features were extracted from the sensor output distribution at different data acquisition nodes. Subsequently, the SVM classifier was developed to identify multiple damage states. A data fusion model was proposed to increase the classification performance. The results indicate that the models have acceptable detection performance, specific ally for cracks larger than 10 mm. The best classification performance was obtained using the information from a group of sensors located near the damage zone.

The potential return of Mars sample material is of great interest to the planetary science community, as it would enable extensive analysis of samples with highly sensitive laboratory instruments. It is important to make sure such a mission concept would not bring any living microbes, which may possibly exist on Mars, back to Earth’s environment. In order to ensure the isolation of Mars microbes from Earth’s Atmosphere, a brazing sealing and sterilizing technique was proposed to break the Mars-to-Earth contamination path. Effectively, heating the brazing zone in high vacuum space and controlling the sample temperature for integrity are key challenges to the implementation of this technique. The break-thechain procedures for container configurations, which are being considered, were simulated by multi-physics finite element models. Different heating methods including induction and resistive/radiation were evaluated. The temperature profiles of Martian samples in a proposed container structure were predicted. The results show that the sealing and sterilizing process can be controlled such that the samples temperature is maintained below the level that may cause damage, and that the brazing technique is a feasible approach to breaking the contamination path.

High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO’s potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.

Laser ultrasonic scanning, especially full-field wave propagation imaging, is attractive for damage detection due to its noncontact nature, sensitivity to local damage, and high spatial resolution. However, its practicality is limited because scanning at a high spatial resolution demands a prohibitively long scanning time. Inspired by binary search, an accelerated laser scanning technique is developed to localize and visualize damage with reduced scanning points and scanning time. The distance between the excitation point and the sensing point during scanning is fixed in this technique to maintain a high signal-to-noise ratio for measured ultrasonic responses. First, the approximate damage boundary is identified by examining the interactions between the ultrasonic waves and damage at the sparse scanning points that are selected by the binary search algorithm. Here, a time-domain laser ultrasonic response is transformed into a spatial ultrasonic domain using a basis pursuit approach so that the interactions between the ultrasonic waves and damage, such as reflections and transmissions, can be better identified in the spatial ultrasonic domain. Then, the region inside the identified damage boundary is visualized as damage. The performance of the proposed accelerated laser scanning technique is validated through the experiment performed on an aluminum plate with a crack. The number of scanning points that is necessary for damage localization and visualization is dramatically reduced from N·M to 4log₂N· log₂M even for the worst case scenario. N and M represent the number of equally spaced scanning points in the x and y directions, respectively, which are required to obtain full-field wave propagation images of the target inspection region.

In this paper, a self-diagnosis technique is introduced for structural health monitoring based on the timefrequency analysis of electromechanical impedance (EMI) signatures. It is first shown that the EMI signature is essentially a pulse-echo signal represented in the frequency domain, and it can be converted into time domain using the convolution theorem. The time-frequency plot is then generated from the recovered time domain signals to cover a wide range of excitation frequencies and provide a more comprehensive damage detection capability. Presenting the EMI signal in the time and the time-frequency domains provides the physical insights that explain how different factors influence the EMI signature. As such, the time domain signal acquired from the EMI is divided into “resonant phase” and “echo phase”. The resonant phase includes the immediate response of the sensor to the excitation and is used to monitor the sensor bonding layer condition, while the echo phase only includes wave reflections from structural damages and boundaries, and is implemented for the structural damage detection. Finally, the proposed method is implemented on a beam structure to detect and localize structural damage in the presence of a damage in the sensor bonding layer.

Technologies based on optical fibers provide the possibility of installing relatively dense networks of sensors that can perform effective strain sensing functions during the operational life of structures. A contemporary trend is the increasing adoption of composite materials in aerospace constructions, which leads to structural architectures made of large monolithic elements. The paper is aimed at showing the feasibility of a detailed reconstruction of the strain field in a composite spar, which is based on the development of reference finite element models and the identification of load modes, consisting of a parameterized set of forces. The procedure is described and assessed in ideal conditions. Thereafter, a surrogate model is used to obtain realistic representation of the data acquired by the strain sensing system, so that the developed procedure is evaluated considering local effects due to the introduction of loads, significant modelling discrepancy in the development of the reference model and the presence of measurement noise. Results show that the method can obtain a robust and quite detailed reconstruction of strain fields, even at the level of local distributions, of the internal forces in the spars and of the displacements, by identifying an equivalent set of load parameters. Finally, the trade-off between the number of sensor and the accuracy, and the optimal position of the sensors for a given maximum number of sensors is evaluated by performing a multi-objective optimization, thus showing that even a relative dense network of externally applied sensors can be used to achieve good quality results.

Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer’s skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

This paper presents a probabilistic model, called Bayesian networks, to visually assess the state of damage in reinforced concrete shear walls. The goal of this research is to reduce the inspection time and decrease the chance of missing or underestimating the state of damage in such structures. To develop this model, we define six types of visible damage on concrete shear walls. The model describes the causal relationship of such damage signs with the design parameters and damage states of the walls. To train and test the model, a database of all visually documented experimental works on concrete shear walls was collected from the literature. The model is trained on ninety percent of the database, and its performance is successfully validated on the ten percent remaining unseen portion of the database. The results show that the model can classify the images of yielded and failed walls. Additionally, it can prognosticate the most probable failure scenario for a yielded wall.

Geotechnical structural elements are used to underpin heavy structures or to stabilize slopes and embankments. The bearing capacity of these components is usually verified by geotechnical load tests. It is state of the art to measure the resulting deformations with electronic sensors at the surface and therefore, the load distribution along the objects cannot be determined. This paper reports about distributed strain measurements with an optical backscatter reflectometer along geotechnical elements. In addition to the installation of the optical fiber in harsh field conditions, results of investigations of the fiber optic system in the laboratory and the most significant results of the field trials are presented.

This paper proposes a new damper that can change the damping force depending on the response displacement and response velocity. The proposed damper reduces the damage of seismic-isolated structures which undergo excessive deformation during huge earthquakes, without lowering the performance of the seismic-isolation system during medium to small magnitude earthquakes. We investigate the effects of using the proposed attenuator on the responses of a superstructure model to seismic motion that causes collision with retaining walls. An experiment using a shaking table is conducted, and the results from the test are compared with those from numerical analyses. The test results agree approximately with the numerical analysis results except for the absolute acceleration results in collision cases.

Damage identification to be used in a SHM system, numerous methods have been studied including methods based on modal properties (natural frequencies and mode shapes) and frequency response function (FRF). However, the methods based on modal properties may have limited capabilities for early detection of local damage due to contaminated measurements and computational error that can lead to false alarm. Besides, local damage is frequently associated with higher modes and nonlinearities and even more difficult to detect. For random, non-stationary, nonlinear, and transient signals, under pattern-level data analysis wavelet transform (WT) has been used as a signal pattern recognition method in structural damage identification. In this study, wavelet decomposition of dynamic data using modified complex Morlet wavelet with variable central frequency (MCMW+VCF) establishes a time-frequency representation for modal parameter estimation and system damage identification. Seismic response data collected from two different steel frames, a twin tower steel structure and a 3-story steel structure, under a series of both white noise and earthquake excitation were used for this discussion. First, the accuracy of MCMW+VCF for the purpose of using in the analysis of nonlinear response of structure is examined through the decomposition of vibration responses into wavelet coefficient distribution as a joint function of time and frequency. Using the wavelet coefficients, phase information and Novelty index, temporal variation of stiffness in the response of structures to strong earthquake excitation can be identified. Besides, through the total wavelet coefficient joint density function at each measurement for the pre-damage and post-damage states, detection and localization of damage can be implemented.

This paper presents the detections of the subsurface features and distresses in roadways and bridge decks from ground penetrating radar (GPR) data collected at traffic speed. This GPR system is operated at 2 GHz with a penetration depth of 60 cm in common road materials. The system can collect 1000 traces a second, has a large dynamic range and compact packaging. Using a four channel GPR array, dense spatial coverage can be achieved in both longitudinal and transversal directions. The GPR data contains significant information about subsurface features and distresses resulting from dielectric difference, such as distinguishing new and old asphalt, identification of the asphalt-reinforced concrete (RC) interface, and detection of rebar in bridge decks. For roadways, the new and old asphalt layers are distinguished from the dielectric and thickness discontinuities. The results are complemented by surface images of the roads taken by a video camera. For bridge decks, the asphalt-RC interface is automatically detected by a cross correlation and Hilbert transform algorithms, and the layer properties (e.g., dielectric constant and thickness) can be identified. Moreover, the rebar hyperbolas can be visualized from the GPR B-scan images. In addition, the reflection amplitude from steel rebar can be extracted. It is possible to estimate the rebar corrosion level in concrete from the distribution of the rebar reflection amplitudes.

The authors have recently proposed a hybrid dense sensor network consisting of a novel, capacitive-based thin-film electronic sensor for monitoring strain on mesosurfaces and fiber Bragg grating sensors for enforcing boundary conditions on the perimeter of the monitored area. The thin-film sensor monitors local strain over a global area through transducing a change in strain into a change in capacitance. In the case of bidirectional in-plane strain, the sensor output contains the additive measurement of both principal strain components. When combined with the mature technology of fiber Bragg grating sensors, the hybrid dense sensor network shows potential for the monitoring of mesoscale systems. In this paper, we present an algorithm for the detection, quantification, and localization of strain within a hybrid dense sensor network. The algorithm leverages the advantages of a hybrid dense sensor network for the monitoring of large scale systems. The thin film sensor is used to monitor strain over a large area while the fiber Bragg grating sensors are used to enforce the uni-directional strain along the perimeter of the hybrid dense sensor network. Orthogonal strain maps are reconstructed by assuming different bidirectional shape functions and are solved using the least squares estimator to reconstruct the planar strain maps within the hybrid dense sensor network. Error between the estimated strain maps and measured strains is extracted to derive damage detecting features, dependent on the selected shape functions. Results from numerical simulations show good performance of the proposed algorithm.

This paper presents the fabrication, modeling and testing of a metamaterial based passive wireless temperature sensor consisting of an array of closed ring resonators (CRRs) embedded in a dielectric material matrix. A mixture of 70 vol% Boron Nitride (BN) and 30 vol% Barium Titanate (BTO) is used as the dielectric matrix and copper washers are used as CRRs. Conventional powder compression is used for the sensor fabrication. The feasibility of wireless temperature sensing is demonstrated up to 200 C. The resonance frequency of the sensor decreases from 11.93 GHz at room temperature to 11.85 GHz at 200 C, providing a sensitivity of 0.462 MHz/C. The repeatability of temperature sensing tests was carried out to quantify the repeatability. The highest standard deviation observed was 0.012 GHz at 200 C.

Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade's surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage.

Particle image velocimetry is an experimental technique for measuring the velocity field of a moving fluid by tracking small particles dispersed within the flow. To extend this technique to the study of fluid temperature, we propose a novel tracer particle which enables direct measurement of the velocity, while acting as a temperature sensor by increasing its fluorescence intensity when the local fluid temperature rises above 32°C. Thermoresponsive tracers are prepared by incorporating nitrobenzofurazan functionalized hydrogels within optically transparent polydimethylsiloxane microspheres. We demonstrate the application of the tracers in the study of forced thermal convection in water around a heated cylinder in an open channel.

Hybrid nanocomposites with carbon nanotubes and graphitic platelets as fillers are known to exhibit remarkable electrical and mechanical properties with many potential strain and damage sensing applications. In this work, we fabricate hybrid nanocomposites with carbon nanotube sheet and coarse graphite platelets as fillers with epoxy matrix. We then examine the electromechanical behavior of these nanocomposites under dynamic loading. The electrical resistivity responses of the nanocomposites are measured in frequency range of 1 Hz to 50 Hz with different levels of induced strains. Axial cycling loading is applied using a uniaxial electrodynamic shaker, and transverse loading is applied on end-clamped specimen using modified speakers. In addition, a dynamic mechanical analysis of nanocomposite specimen is performed to characterize the thermal and dynamic behavior of the nanocomposite. Our results indicate that these hybrid nanocomposites exhibit a distinct piezoresistive response under a wide range of dynamic loading conditions, which can be beneficial for potential sensing applications.

The field of event classification and localization in building environments using accelerometers has grown significantly due to its implications for energy, security, and emergency protocols. Virginia Tech’s Goodwin Hall (VT-GH) provides a robust testbed for such work, but a reduced scale testbed could provide significant benefits by allowing algorithm development to occur in a simplified environment. Environments such as VT-GH have high human traffic that contributes external noise disrupting test signals. This paper presents a design solution through the development of an isolated platform for data collection, portable demonstrations, and the development of localization and classification algorithms. The platform’s success was quantified by the resulting transmissibility of external excitation sources, demonstrating the capabilities of the platform to isolate external disturbances while preserving gait information. This platform demonstrates the collection of high-quality gait information in otherwise noisy environments for data collection or demonstration purposes.

In this paper, we introduce a method for estimating human left/right walking gait balance using footstep-induced structural vibrations. Understanding human gait balance is an integral component of assessing gait, neurological and musculoskeletal conditions, overall health status, and risk of falls. Existing techniques utilize pressure- sensing mats, wearable devices, and human observation-based assessment by healthcare providers. These existing methods are collectively limited in their operation and deployment; often requiring dense sensor deployment or direct user interaction. To address these limitations, we utilize footstep-induced structural vibration responses. Based on the physical insight that the vibration energy is a function of the force exerted by a footstep, we calculate the vibration signal energy due to a footstep and use it to estimate the footstep force. By comparing the footstep forces while walking, we determine balance. This approach enables non-intrusive gait balance assessment using sparsely deployed sensors. The primary research challenge is that the floor vibration signal energy is also significantly affected by the distance between the footstep location and the vibration sensor; this function is unclear in real-world scenarios and is a mixed function of wave propagation and structure-dependent properties. We overcome this challenge through footstep localization and incorporating structural factors into an analytical force-energy-distance function. This function is estimated through a nonlinear least squares regression analysis. We evaluate the performance of our method with a real-world deployment in a campus building. Our approach estimates footstep forces with a RMSE of 61.0N (8% of participant's body weight), representing a 1.54X improvement over the baseline.

Recent years have shown prolific advancements in smart infrastructures, allowing buildings of the modern world to interact with their occupants. One of the sought-after attributes of smart buildings is the ability to provide unobtrusive, indoor localization of occupants. The ability to locate occupants indoors can provide a broad range of benefits in areas such as security, emergency response, and resource management. Recent research has shown promising results in occupant building localization, although there is still significant room for improvement. This study presents a passive, small-scale localization system using accelerometers placed around the edges of a small area in an active building environment. The area is discretized into a grid of small squares, and vibration measurements are processed using a pattern matching approach that estimates the location of the source. Vibration measurements are produced with ball-drops, hammer-strikes, and footsteps as the sources of the floor excitation. The developed approach uses matched filters based on a reference data set, and the location is classified using a nearest-neighbor search. This approach detects the appropriate location of impact-like sources i.e. the ball-drops and hammer-strikes with a 100% accuracy. However, this accuracy reduces to 56% for footsteps, with the average localization results being within 0.6 m (α = 0.05) from the true source location. While requiring a reference data set can make this method difficult to implement on a large scale, it may be used to provide accurate localization abilities in areas where training data is readily obtainable. This exploratory work seeks to examine the feasibility of the matched filter and nearest neighbor search approach for footstep and event localization in a small, instrumented area within a multi-story building.

In this study, based on human knee’s kinetics, a smart prosthetic knee employing springs, DC motor and magnetorheological (MR) damper is designed. The MR damper is coupled in series with the springs that are mounted in parallel with the DC motor. The working principle of the prosthesis during level-ground walking is presented. During stance phase, the MR damper is powered on. The springs will store and release the negative mechanical energy for restoring the function of human knee joint. In swing phase, the MR damper is powered off for disengaging the springs. In this phase, the work of knee joint is negative. For improving the system energy efficiency, the DC motor will work as a power generator to supply required damping torque and harvest electrical energy. Finally, the design of MR damper is introduced.

In order to substitute a conventional fin operated by hydraulic actuators, a smart fin actuated by piezoelectric material was attempted. A straight unimorph actuator was embedded along the spanwise direction in the hollow inner space of the fin. A hinge which was constrained except the axial rotation was located at the 1/4^th chord line, and this enabled the smart fin to rotate in its rigid pitch direction. In this paper, starting from the fundamental structural analysis, the aeroelastic and aeroservoelastic stability, and structural response simulations of the smart fin control system were performed by integration of MSC.NASTRAN, ZAERO and MATLAB/Simulink. The controller was designed to ensure the flight stability and to maintain the pitch angles of the smart fin under specific flight conditions. Closed-loop control system of the smart fin was constructed and analyzed by MATLAB/Simulink.

The evolution of additive manufacturing has allowed engineers to use 3D printing for many purposes. As a natural consequence of the 3D printing process, the printed object is anisotropic. As part of an ongoing project to embed piezoelectric devices in 3D printed structures for structural health monitoring (SHM), this study aims to find the mechanical properties of the 3D printed material and the influence of different external factors on those properties. The orthotropic mechanical properties of a 3D printed structure are dependent on the printing parameters used to create the structure. In order to develop an orthotropic material model, mechanical properties will be found experimentally from additively manufactured samples created from polylactic acid (PLA) using a consumer-level fused deposition modeling (FDM) printer; the Lulzbot TAZ 6. Nine mechanical constants including three Young's moduli, three Poisson’s ratios, and three shear moduli are needed to fully describe the 3D elastic behavior of the material. Printed specimens with different raster orientations and print orientations allow calculation of the different material constants. In this work, seven of the nine mechanical constants were found. Two shear moduli were unable to be measured due to difficulties in printing two of the sample orientations. These mechanical properties are needed in order to develop orthotropic material models of systems employing 3D printed PLA. The results from this paper will be used to create a model of a piezoelectric transducer embedded in a 3D printed structure for structural health monitoring.

In this work, we validate the behavior of 3D Photonic Crystals for Structural Health Monitoring applications. A Finite Difference Time Domain (FDTD) analysis has been performed and compared to experimental data. We demonstrate that the photonic properties of a crystal (comprised of sub-micrometric polystyrene colloidal spheres embedded in a PDMS matrix) change as a function of the axial strain applied to a rubber substrate. The change in the reflected wavelength, detected through our laboratory experiments and equivalent to a visible change in crystal color, is assumed to be caused by changes in the interplanar spacing of the polystyrene beads. This behavior is captured by our full wave 3D FDTD model. This contains different wavelengths in the visible spectrum and the wave amplitudes of the reflected and transmitted secondary beams are then computed. A change in the reflectance or transmittance is observed at every programmed step in which we vary the distance between the spheres. These investigations are an important tool to predict, study and validate our understanding of the behavior of this highly complex physical system. In this context, we have developed a versatile and robust parallelized code, able to numerically model the interaction of light with matter, by directly solving Maxwell's equations in their strong form. The ability to describe the physical behavior of such systems is an important and fundamental capability which will aid the design and validation of innovative photonic sensors.

In this paper a new equivalent circuit is presented which describes the dynamics of a prototype micro-gyro sensor. The concept takes advantage of the principles employed in vibratory gyro sensors and the ductile attributes of GalFeNOL to target high sensitivity and shock tolerance. The sensor is designed as a tuning fork structure. A GalFeNOL patch attached to the y-z surface of the drive prong causes both prongs to bending the x-z plane (about the y axis) and a patch attached to the x-z surface of the sensing prong detects Coriolis-force induced bending in the y-z plane (about the x axis). A permanent magnet is bonded on top of each prong to give bias magnetic fields. A solenoid coil surrounding the drive prong is used to produce bending in the x-z plane of both prongs. The sensing prong is surrounded by a solenoid coil with N turns in which a voltage proportional to the time rate of change of magnetic flux is induced.

The equivalent circuit enables the efficient modeling of a gyro sensor and an electromechanical behavioral simulation using the circuit simulator SPICE. The prongs are modeled as wave guiding bending beams which are coupled to the electromagnetic solenoid coil transducer. In contrast to known network approaches, the proposed equivalent circuit is the first tuning fork model, which takes full account of the fictitious force in a constant rotating frame of reference. The Coriolis force as well as the centrifugal force on a concentrated mass are considered.

Mircostructured optical fibers have recently been considered as platforms for microfluidic devices for boimedical and manufacturing process control applications. More specifically they contribute to the sub-discipline of optofluidics were submicroliter volumes of fluids (i.e. liquids and gases) are manipulated in the micron sized channels inside of these fibers for enhanced optical detection. A special category of these fibers contain multiple cores that allow for interferometric measurement of high temperatures up to 1000°C. At low temperatures less than 50°C, coupling conditions between neighboring cores may be affected by transient external and internal thermal changes. Measurements of the coupling characteristics for a 3-core microstructured optical fibers are presented for temperatures up to 50°C. Multicore fibers with core separations of 2.5 μm and 5 μm were investigated. Typical inter-core coupling is governed by the separation distance of neighboring cores and conventional waveguide conditions. However, it was observed that the coupling conditions responded to thermal changes in the optical characteristics of the solid cores. Images of the mode field distribution were acquired for a range of temperatures during a 5 s period. The intensity was determined for temperatures between 25°C and 50.9°C. Significant time-dependent intercore-coupling instabilities were present for temperatures less than 22°C. The intensity of the coupled light varied according to microchannel air-flow rates of 1-2mm/s. The findings presented will be helpful in determining steady-state thermal conditions for microstructured optical fibers. Real-time and high speed sensing applications may benefit from the investigation of the coupling characteristics for temperatures less than 100°C.

Non-destructive testing on wire rope is in great demand to prevent safety accidents at sites where many heavy equipment using ropes are installed. In this paper, a research on quantification of magnetic flux leakage (MFL) signals were carried out to detect damages on wire rope. First, a simulation study was performed with a steel rod model using a finite element analysis (FEA) program. The leakage signals from the simulation study were obtained and it was compared for parameter: depth of defect. Then, an experiment on same conditions was conducted to verify the results of the simulation. Throughout the results, the MFL signal was quantified and a wire rope damage detection was then confirmed to be feasible. In further study, it is expected that the damage characterization of an entire specimen will be visualized as well.

A spectral profile division multiplexed fiber Bragg grating (FBG) sensor network is described in this paper. The unique spectral profile of each sensor in the network is identified as a distinct feature to be interrogated. Spectrum overlap is allowed under working conditions. Thus, a specific wavelength window does not need to be allocated to each sensor as in a wavelength division multiplexed (WDM) network. When the sensors are serially connected in the network, the spectrum output is expressed through a truncated series. To track the wavelength shift of each sensor, the identification problem is transformed to a nonlinear optimization problem, which is then solved by a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO). To demonstrate the application of the developed network, a network consisting of four FBGs was integrated into a Kevlar woven fabric, which was under a quasi-static load imposed by an impactor head. Due to the substantial radial strain in the fabric, the spectrums of different FBGs were found to overlap during the loading process. With the developed interrogating method, the overlapped spectrum would be distinguished thus the wavelength shift of each sensor can be monitored.

The main objective of this research is to develop a noncontact and noninvasive method for monitoring infections at the interface of human tissue and osseointegrated prostheses. The technique used here is centered on the theory of a noncontact permittivity imaging technique known as electrical capacitance tomography (ECT). This work is divided into two main parts. First, an ECT electrical permittivity reconstruction software and hardware system was developed. Second, a carbon nanotube-polyaniline nanocomposite thin film was designed and fabricated such that its electrical permittivity is sensitive to pH stimuli. The dielectric properties of this thin film were characterized as it was exposed to different pH buffer solutions. It is envisioned that osseointegrated implants can be pre-coated with the pH-sensitive nanocomposite prior to implant. When infection occurs and alters the local pH of tissue at the human-prosthesis interface, the dielectric property of the film would change accordingly. Then, ECT can interrogate the cross-section of the human limb and reconstruct its permittivity distribution, revealing localized changes in permittivity due to infection. To validate this concept, a prosthesis phantom was coated with the nanocomposite pH sensor and then immersed in different pH buffer solutions. ECT was conducted, and the results showed that the magnitude and location of subsurface, localized, pH changes could be detected. In general, noncontact tomography coupled with stimuliresponsive thin films could pave way for new modalities of noninvasive human body imaging, in particular, for patients with osseointegrated implants and prostheses.

Fiber Bragg grating (FBG) and other fiber optic based sensors have been used to sense environmental parameters for numerous applications including aerospace, oil and gas, civil structure health monitoring, mining, and medical. There are many benefits to using fiber optic based sensors over traditional electrical sensing methods. These advantages include: immunity to electromagnetic interference, high bandwidth, low loss, small, lightweight, and portability. New developments allow these physical measurements such as strain, temperature, pressure, vibration, and acoustics to be made at extremely fast speeds extending the capability of fiber optic sensor systems to monitor impacts and other rapid events.

A fiber optic based corrosion sensor utilizing the magnetic interaction force is proposed. The sensor aims to detect corrosion in structures, which are made from ferromagnetic materials (e.g. pipelines made of carbon steel). It consists of a beam that is made from a non-corrosive material with embedded Fiber Bragg Grating (FBG) sensor and a permanent magnet. The beam is placed in a position such that the permanent magnet is within a few millimeters away from the ferromagnetic material to allow for the generation of the magnetostatic attraction force between the sensor and the pipe. The corrosion causes a reduction in material thickness, which increases the distance between the material and the magnet, thus the attraction force will decrease. This change in the force can be related directly to the change in the strain measured by the optical fiber as it causes a shift in the wavelength of the reflected light. We present initial analytical investigation of the feasibility of the proposed concept for practical application of monitoring the external corrosion process on the exposed pipelines. The estimated magnitude of strain change due to corrosion is within the measurement range of typical FBG strain sensors.

In this paper, ceria nanoparticles are studied as optical probe for different types of metallic tiny particles via fluorescence quenching technique. The synthesized ceria nanoparticles are formed to have charged oxygen vacancies which can be considered the receptors for metal tiny particles to be sensed or adsorbed. Under near-UV excitation, the visible fluorescent emission intensity is found to be reduced with increasing the concentration of tiny particles in the colloidal synthesized nanoparticles' solution. Stern-Volmer constants, which are considered as an indication for the sensitivity to quenchers, have been calculated for the used ceria nanoparticles. This work could be further helpful in many applications such as, sensitive optical sensors in both biomedical and environmental applications.

The use of inflatable structures in aerospace applications is becoming increasingly widespread. Monitoring the inflation status and overall health of these inflatables requires an accurate means of shape sensing. This work reports on the application of local curvature sensing to establish global shape. The technique uses surface-attached Fiber Bragg Grating (FBG) curvature sensing rosettes consisting of three curvature-sensing FBG pairs. Presented will be methods of extracting values for the curvature tensor using the curvature-sensing rosette, along with experimental verification. By using such rosettes on an inflatable surface, the geometry of the inflatable can be monitored in real time.

Fibre Bragg Grating (FBG) sensors are increasingly being used on a wide range of civil, industrial and aerospace structures. The sensors are created inside optical fibres (usually standard telecommunication fibres); the optical fibres technology allows to install the sensors on structures working in harsh environments, since the materials are almost insensitive to corrosion, the monitoring system can be positioned far away from the sensors without sensible signal losses, and there is no risk of electric discharge. FBG sensors can be used to create strain gages, thermometers or accelerometers, depending on the coating on the grating, on the way the grating is fixed to the structure, and on the presence of a specifically designed interface that can act as a transducer. This paper describes a test of several different FBG sensors to monitor an high pressure pipe that feeds the hydraulic actuators of a 6 degrees-of-freedom shaking table at the ENEA Casaccia research centre. A bare FBG sensor and a copper coated FBG sensor have been glued on the pipe. A third sensor has been mounted on a special interface to amplify the vibrations; this last sensor can be placed on the steel pipe by a magnetic mounting system, that also allows the its removal. All the sensor are placed parallel to the axis of the pipe. The analysis of the data recorded when the shaking table is operated will allow to determine which kind of sensor is best suited for structural monitoring of high pressure pipelines.

Soft coatings are popularly applied in industry to prevent corrosion for structural steel. However, coatings cannot fully prevent corrosion from happening. In this paper, an inline corrosion monitoring system for steel was extended to steel with soft coating based on fiber Bragg grating sensors (FBGs). Experimental results show that an increase in central wavelength of FBGs can be detected if corrosion occurs under soft coatings. A positive relationship between the status of corrosion and the increase in central wavelength was also observed. This study revealed the potential of FBGs to serve as corrosion assessment technique for steel protected with soft coatings.

Pitting corrosion is difficult to identify through visual inspection and can lead to sudden structural failures. As such, an experimental study was undertaken to investigate whether distributed fiber optic strain sensors are capable of detecting the locations and strain changes associated with stirrup corrosion in reinforced concrete beams. In comparison to conventional strain gauges, this type of sensor can measure the strain response along the entire length of the fiber optic cable. Two specimens were tested: a control and a deteriorated beam. The deteriorated beam was artificially corroded by reducing the cross sectional area of the closed stirrups by 50% on both sides of the stirrup at the mid-height. This level of area reduction represents severe pitting corrosion. The beams were instrumented with nylon coated fiber optic sensors to measure the distributed strains, and then tested to failure under three point bending. The load deflection behavior of the two specimens was compared to assess the impact of the artificial pitting corrosion on the capacity. Digital Image Correlation was used to locate the extent and trajectory of the crack paths. It was found that the pitting corrosion had no impact on capacity or stiffness. Also, in this investigation the fiber optic sensing system failed to detect the location and strain changes due to pitting corrosion since the shear cracks did not intersect with the pitting location.

In this paper, a novel design of fiber Bragg grating tilt sensor is proposed. This tilt sensor exhibits high angle sensitivity and resolution. The presented tilt sensor works on the principle of the force of buoyancy in a liquid. It has certain advantages over the other designs of tilt sensors. The temperature effect can be easily compensated by using an un-bonded or free FBG. An analytical model is established which correlates the Bragg wavelength (λ_B) with the angle of inclination. This model is then validated by the experiment, where the experimental and analytical results are found in good agreement with each other.

In this paper, we have demonstrated that the spectral bandwidth of a FBG can be modulated by embedding a single Fiber Bragg grating (FBG) sensor in a composite laminate. The bandwidth modulation was achieved by exploiting the birefringence of the embedded – FBG sensor, which was sensitive to the transverse strain and thus varies with temperature or axial load. Combining the bandwidth changes with the Bragg wavelength shift may enable measuring the simultaneous changes in temperature and axial strain using a single FBG sensor.

Conventional non-destructive inspection approaches can be costly, require physical access to the subject and some of the established inspection methods are more difficult to implement on polymer composite materials. This has driven a growing interest in the use of embedded sensors. The physical form of optical fibres means they are well suited to embedment in fibre reinforced composites however there are technical challenges associated with their use.

The non-uniform geometry of woven fabric composite materials can induce localised macro bending in embedded optical fibre Bragg grating (FBG) sensors when they are compacted between layers during the lay-up process. This leads to a non-uniform strain profile along the optical fibres which can limit the efficacy of conventional peak tracking algorithms for demodulating strain.

This paper investigates the effect of gauge length on sensor response for FBGs of different length embedded in a woven glass fibre reinforced composite coupon. The experimentally measured FBG reflection spectra were compared to model predictions for the unloaded state assuming an FBG bend radius of similar dimensions to the weft of the fabric. Through thickness fibre optic strains under four point loading conditions were compared to side-imaged thermoelastic response measurements. The results show that the ratio of the gauge length to the curvature radius of the macro bending is critical with the optimal gauge length being a compromise between FBG reflectivity and sensor response.

The ability to quantitatively assess the condition of railroad bridges facilitates objective evaluation of their robustness in the face of hazard events. Of particular importance is the need to assess the condition of railroad bridges in networks that are exposed to multiple hazards. Data collected from structural health monitoring (SHM) can be used to better maintain a structure by prompting preventative (rather than reactive) maintenance strategies and supplying quantitative information to aid in recovery. To that end, a wireless monitoring system is validated and installed on the Harahan Bridge which is a hundred-year-old long-span railroad truss bridge that crosses the Mississippi River near Memphis, TN. This bridge is exposed to multiple hazards including scour, vehicle/barge impact, seismic activity, and aging. The instrumented sensing system targets non-redundant structural components and areas of the truss and floor system that bridge managers are most concerned about based on previous inspections and structural analysis. This paper details the monitoring system and the analytical method for the assessment of bridge condition based on automated data-driven analyses. Two primary objectives of monitoring the system performance are discussed: 1) monitoring fatigue accumulation in critical tensile truss elements; and 2) monitoring the reliability index values associated with sub-system limit states of these members. Moreover, since the reliability index is a scalar indicator of the safety of components, quantifiable condition assessment can be used as an objective metric so that bridge owners can make informed damage mitigation strategies and optimize resource management on single bridge or network levels.

The demand on high-speed weigh-in-motion (WIM) measurement rises significantly in last decade to collect weight information for traffic managements especially after the introduction of weigh-station bypass programs such as Pre-Pass. In this study, a three-dimension glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3D GFRP-FBG) is introduced to be embedded inside flexible pavements for weigh-in-motion (WIM) measurement at high speed. Sensitivity study showed that the developed sensor is very sensitive to the passing weights at high speed. Field tests also validated that the developed sensor was able to detect weights at a vehicle driving speed up to 55mph, which can be applied for WIM measurements at high speed.

Coastal infrastructure, such as bridges, are susceptible to many forms of coastal hazards: particularly hurricane surge and wave loading. These two forms of loading can cause catastrophic damage to aging highway infrastructure. It is estimated that storm damage costs the United States about $50 Billion per year. In light of this, it is crucial that we understand the damaging forces placed on infrastructure during storm events so that we can develop safer and more resilient coastal structures. This paper presents the ongoing research to enable the efficient collection of extreme event loads acting on both the substructure and superstructure of low clearance, simple span, reinforced concrete bridges. Bridges of this type were commonly constructed during the 1950’s and 60’s and are particularly susceptible to deck unseating caused by hurricane surge and wave loading. The sensing technology used to capture this data must be ruggedized to survive in an extremely challenging environment, be designed to allow for redundancy in the event of sensors or other network components being lost in the storm, and be relatively low cost to allow for more bridges to be instrumented per storm event. The prototype system described in this paper includes wireless technology, rapid data transmission, and, for the sensors, self-contained power. While this specific application focuses on hurricane hazards, the framework can be extended to include other natural hazards.

Here we will propose the conceptual new idea of the inspection of concrete bridge using smart materials and mobile IoT system. We apply opal photonic crystal film to detect cracks on concrete infrastructures. High quality opal photonic crystal films were coated on black color PET sheet over 1000 cm² area. The opal film sheet was cut and adhered to concrete or mortar test pieces by epoxy resin. In the tensile test, the structural color of the opal sheet was changed when the crack was formed. As a demonstration, we have installated the opal film sheet on the wall of the concrete bridge. Our final purpose is the color change will be recorded by portable CCD devices, and send to expert via IoT network.

Currently, there have been plenty of researches conducted for two main problems in structural health monitoring, damage and vehicle parameter identification on bridges. However, only a handful of methods could achieve these two functions synchronously, which would cause the redundancy of sensors also the rise of cost in monitoring system. In this paper, a method to identify damage and vehicle parameters, such as axle load, wheelbase and velocity on a bridge simultaneously was proposed, based on the influence line of long-gauge strain. The influence line of long-gauge strain was derived primarily according to conventional strain influence line theory, at the basis of which the relationships among the local element bending stiffness of bridge, vehicle parameters and long-gauge strain were figured out then. The two order difference of long-gauge strain was chosen as the key index to found this identification method. Finally, to verify the reliability of this method, a set of numerical simulations was conducted, whose results showed that this method exhibited good performance.

Regarding Structural Health Monitoring (SHM) for seismic acceleration, Wireless Sensor Networks (WSN) is a promising tool for low-cost monitoring. Compressed sensing and transmission schemes have been drawing attention to achieve effective data collection in WSN. Especially, SHM systems installing massive nodes of WSN require efficient data transmission due to restricted communications capability. The dominant frequency band of seismic acceleration is occupied within 100 Hz or less. In addition, the response motions on upper floors of a structure are activated at a natural frequency, resulting in induced shaking at the specified narrow band. Focusing on the vibration characteristics of structures, we introduce data compression techniques for seismic acceleration monitoring in order to reduce the amount of transmission data. We carry out a compressed sensing and transmission scheme by band pass filtering for seismic acceleration data. The algorithm executes the discrete Fourier transform for the frequency domain and band path filtering for the compressed transmission. Assuming that the compressed data is transmitted through computer networks, restoration of the data is performed by the inverse Fourier transform in the receiving node. This paper discusses the evaluation of the compressed sensing for seismic acceleration by way of an average error. The results present the average error was 0.06 or less for the horizontal acceleration, in conditions where the acceleration was compressed into 1/32. Especially, the average error on the 4th floor achieved a small error of 0.02. Those results indicate that compressed sensing and transmission technique is effective to reduce the amount of data with maintaining the small average error.

There is a need for sample verification and mass quantification of rock, soil and/or ice obtained by sample acquisition mechanisms on extraterrestrial bodies. For many scientific instruments information about the mass of the sample would aid in the interpretation of the data and help prevent the portioning system from overloading instrument ports. Additionally, on a potential sample return mission it is likely that a sample confirmation or mass determination requirement would be implemented before the spacecraft would be commanded to return to Earth or Lunar orbit. In an effort to meet these potential requirements, a piezoelectric resonance balance is being developed to measure a frequency change proportional to the sample mass change. In previous work¹ we developed a resonance balance which produced large non-linear frequency changes due to the addition of a large mass. In this study we have looked at a variety of resonator geometries in an effort to linearize the frequency shift with mass. In addition, we have investigated the use of oscillator/counter circuitry to track the frequency shift of the piezoelectric mass balance. In this new design the frequency shifts automatically when a mass is placed on the balance and the counter circuit calculates the frequency shift. This frequency is then converted to a mass using calibration tables determined previously. An additional feature we have implemented is the use of a high frequency thickness mode piezoelectric resonator to mix the sample and a reactant or solvent. This allows for measuring both sample and reagent prior to ingestion by the instrument. This paper will focus on the design requirements and how they are affected by the local gravity and acoustic properties of the sample. Designs which allow for easy loading and unloading of the balance will also be discussed.

Energy harvesting from an acoustic field is challenging given the low energy density available in most acoustic phenomena. A notable exception is in the domain of pumped, pressurized fluids, where acoustic pressure amplitudes may be on the order of 5~10% of the mean static pressure, in some applications reaching mega-pascal amplitudes, corresponding to acoustic intensities advantageous for energy harvesting. However, the static pressures that are common within pressurized systems require mechanically robustness for pressure containment, which prevents the use of common energy harvester configurations. Nonetheless, energy densities may be high enough such that non-resonant configurations are feasible; and, the fact that the acoustic pressure within pumped systems typically has a relatively narrow band spectrum means that power conditioning circuits may be optimized for power conversion. With power available from the pumped fluid itself, through what is termed a Hydraulic Pressure Energy Harvester, it then becomes possible to implement self-powered wireless sensing nodes. This paper describes a proof-of-concept HPEH implementation and demonstration of a multi-functional self-powered wireless sensor for use in a hydraulic application.

In this short paper, we report a design of an energy harvesting backpack with a mechanical motion rectifier (MMR). Also, we report the experiment studies conducted on two male subjects who carry two types of energy harvesting backpacks, including the MMR-based and traditional energy harvesting backpack, while walking on a treadmill at 3 and 3.5 mph. The harvested power of the backpacks shunted with different resistors are reported with each walking speed to demonstrate the high efficiency of the MMR-based backpack harvester. During the tests, the MMR-based backpack harvest generated more power regardless of walking speeds and resistor values. Finally, a maximum average power of 4.8 Watts and instant power of 12.8 Watts were obtained by one subject, carrying the MMR-based backpack harvester and walking at 3.5 mph.

Embedding sensors within additive manufactured (AM) structures gives the ability to develop smart structures that are capable of monitoring the mechanical health of a system. AM provides an opportunity to embed sensors within a structure during the manufacturing process. One major limitation of AM technology is the ability to verify the geometric and material properties of fabricated structures. Over the past several years, the electromechanical impedance (EMI) method for structural health monitoring (SHM) has been proven to be an effective method for sensing damage in structurers. The EMI method utilizes the coupling between the electrical and mechanical properties of a piezoelectric transducer to detect a change in the dynamic response of a structure. A piezoelectric device, usually a lead zirconate titanate (PZT) ceramic wafer, is bonded to a structure and the electrical impedance is measured across as range of frequencies. A change in the electrical impedance is directly correlated to changes made to the mechanical condition of the structure. In this work, the EMI method is employed on piezoelectric transducers embedded inside AM parts to evaluate the feasibility of performing SHM on parts fabricated using additive manufacturing. The fused deposition modeling (FDM) method is used to print specimens for this feasibility study. The specimens are printed from polylactic acid (PLA) in the shape of a beam with an embedded monolithic piezoelectric ceramic disc. The specimen is mounted as a cantilever while impedance measurements are taken using an HP 4194A impedance analyzer. Both destructive and nondestructive damage is simulated in the specimens by adding an end mass and drilling a hole near the free end of the cantilever, respectively. The Root Mean Square Deviation (RMSD) method is utilized as a metric for quantifying damage to the system. In an effort to determine a threshold for RMSD, the values are calculated for the variation associated with taking multiple measurements and with re-clamping the cantilever, and determined to be 0.154, and 3.125 respectively. The RMSD value of the cantilever with a 400 g end mass is 11.39, and the RMSD value of the cantilever with a 4 mm hole near the end is 12.15. From these results, it can be determined that the damaged cases have much higher RMSD values than the RMSD values associated with measurements and set up variability of the healthy structure.

A matching pursuit (MP) algorithm is effective tool to decompose the overlapped wave packets in a signal so that each wave mode can be identified. For the successful separations of the wave packets, an atom function should be properly designed, that can well resemble the physical features of the signal of interest. In this paper, a novel atom function for the MP algorithm is proposed based on the wave propagating model due to an excitation of a Hann-windowed toneburst signal, which performs very accurately compared to the MP algorithm with the existing Gaussian-type atom functions. The decomposed wave packets, including the directly scattered wave from damage as well as the reverberant waves from the free edges of the plate, via the MP method are employed in the damage imaging algorithm, highlighting the damaged location with higher intensity than the conventional algorithm utilizing only a direct reflected wave. The proposed approach is verified from the experiment where four piezoelectric wafers can accurately identify the damage location in a plate.

Osseointegrated prostheses which integrate the prosthesis directly to the limb bone are being developed for patients that are unable to wear traditional socket prostheses. While osseointegration of the prosthesis offers amputees improvement in their quality of life, there remains a need to better understand the integration process that occurs between the bone and the prosthesis. Quantification of the degree of integration is important to track the recuperation process of the amputee, guide physical therapy regimes, and to identify when the state of integration may change (due to damage to the bone). This study explores the development of an assessment strategy for quantitatively assessing the degree of integration between an osseointegrated prosthesis and host bone. Specifically, the strategy utilizes a titanium rod prosthesis as a waveguide with guided waves used to assess the degree of integration. By controlling waveforms launched by piezoelectric wafers bonded on the percutaneous tip of the prosthesis, body waves are introduced into the waveguide with wave reflections at the boneprosthesis interface recorded by the same array. Changes in wave energy are correlated to changes at the contact interface between the titanium rod and the bone material. Both simulation and experimental tests are presented in this paper. Experimental testing is performed using a high-density polyethylene (HDPE) host because the elastic modulus and density of HDPE are close to that of human and animal bone. Results indicate high sensitivity of the longitudinal wave energy to rod penetration depth and confinement stress issued by the host bone.

Sensor data validation is a key module in any fault diagnosis system. A pattern recognition based algorithm is therefore proposed to validate the reliability of an arbitrarily distributed sensor network and its data. Single-frequency Lamb wave experiments are conducted on aircraft qualified aluminium plates, which have perfectly bonded and debonded Lead Zirconate Titanate patches along with structural damage in the form of holes. Sensor debondings are examined using sensor network data. Simple features like standard deviation, autocorrelation are extracted from Lamb wave signals and employed in the pattern recognition system that distinctly identifies the sensor debonding albeit the structural damage is present.

We present a novel, optical ultrasound airborne acoustic testing setup exhibiting a frequency bandwidth of 1MHz in air. The sound waves are detected by a miniaturized Fabry-Pérot interferometer (2mm cavity) whilst the sender consists of a thermoacoustic emitter or a short laser pulse We discuss characterization measurements and C-scans of a selected set of samples, including Carbon fiber reinforced polymer (CFRP). The high detector sensitivity allows for an increased penetration depth. The high frequency and the small transducer dimensions lead to a compelling image resolution.

This paper describes reduction of water flow velocity by array of poles as a new wave mitigation structure. This structure is based on tsunami mitigation coastal forest. As natural forests have many problems such as low fraction of trees, low visibility of ocean waves, low strength, long of time to grow, and so on. To cope with these problems, a new wave mitigation structure has been developed, which are intended to add better capability of high wave or tsunami mitigation effect to actual ones by optimizing various parameters such as configuration, distribution density and material properties. In this study, the effect of type of material and its combination were mainly investigated. According to the results, reduction rate of the flow velocity increases with increasing number of rows for each material up to a certain level, and that of poles having lower Young's modulus is generally higher than that of those having higher Young's modulus. The effect of combination of materials was also investigated and drastic increase of mitigation effect was found when soft and hard poles were combined.

techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.

This paper presents a new self-powered hybrid electromagnetic damper that can mitigate vibration of a structure. The damper is able to switch between a power-regenerative passive mode and a semi-active mode depending on the power demand and capacity. The energy harvested in the passive mode due to suppression of vibration is employed to power up the sensing and electronic components necessary for the semi-active control. This provides a hybrid control capability that is autonomous in terms of its power requirement. The device mechanism and the circuitry that can realize this self-powered electromagnetic damper are described in this paper. The parameters that determine the device feasible force region are distinguished. The function of this proposed damper is numerically evaluated by incorporating it for structural vibration reduction in a TMD system under ground motion excitation. It is demonstrated that under a target intensity of earthquake, the damper is inclined to operate more in the semi-active mode as more power flows into the device. This performance gain is attained autonomous without the need of external power supply.

As the number of long-span bridges is increasing worldwide, maintaining their structural integrity and safety become an important issue. Because the stay cable is a critical member in most long-span bridges and vulnerable to wind-induced vibrations, vibration mitigation has been of interest both in academia and practice. While active and semi-active control schemes are known to be quite effective in vibration reduction compared to the passive control, requirements for equipment including data acquisition, control devices, and power supply prevent a widespread adoption in real-world applications. This study develops an integrated system for vibration control of stay-cables using wireless sensors implementing a semi-active control. Arduino, a low-cost single board system, is employed with a MEMS digital accelerometer and a Zigbee wireless communication module to build the wireless sensor. The magneto-rheological (MR) damper is selected as a damping device, controlled by an optimal control algorithm implemented on the Arduino sensing system. The developed integrated system is tested in a laboratory environment using a cable to demonstrate the effectiveness of the proposed system on vibration reduction. The proposed system is shown to reduce the vibration of stay-cables with low operating power effectively.

Spaceborne cooler generates undesirable micro-vibration disturbances during its on-orbit operation and this may seriously affect the image quality of high resolution observation satellites. For the aim of assuring the high quality images of the observation satellite, most of studies have dealt with on-orbit micro-vibration isolation system with a low stiffness, but have not taken launch vibration environments into account. Hence, holding-and-release mechanisms should be considered for guaranteeing the structural safety of the cooler supported by a low stiffness isolator and isolator itself during harsh launch loads. However, this approach has increased the system complexity and lowered its reliability, in addition to increasing the total mass of the system. To overcome this drawback, in this study, a blade type passive vibration isolator employing the characteristics of a pseudoelastic shape memory alloy (SMA) was proposed. This isolator can guarantee structural safety of the cooler and the isolator itself under harsh launch vibration loads without requiring holding-and-release mechanism, while effectively isolating the cooler-induced micro-vibration in on-orbit conditions. The basic characteristics of the isolator were evaluated through a dynamic loading tests in accordance with temperature variations. Based on the measured characteristics, the effectiveness of the isolator design under launch and on-orbit environments was investigated.

The Goodwin Hall Smart Infrastructure facility at Virginia Tech is a five-story “smart building" with an integrated network of 213 wired accelerometers. We utilize a subset of 68 sensors to perform high-resolution Operational Modal Analysis (OMA) of the structure under windy conditions. The low-rise, L-shaped construction and high mass, high stiffness properties of Goodwin Hall provide a unique case study in comparison to typical cases of building OMA in literature, which generally feature high-rise buildings with rectangular architectures. Our work focuses on data acquisition and feature extraction, which are two critical steps within a complete structural health monitoring approach. Our detailed methodology establishes guidelines for sensor selection and data processing applicable to this and more general cases. Modal parameters extraction using Stochastic Subspace Identification shows the first four natural frequencies, damping values, participation factors and mode shapes of the building. We hypothesize that high damping values and large differences in the participation of fundamental modes are related to the nature of the wind excitation.

A sparse reconstruction localization method is proposed, which is capable of localizing multiple acoustic emission events occurring closely in time. The events may be due to a number of sources, such as the growth of corrosion patches or cracks. Such acoustic emissions may yield localization failure if a triangulation method is used. The proposed method is implemented both theoretically and experimentally on large diameter thin-walled pipes. Experimental examples are presented, which demonstrate the failure of a triangulation method when multiple sources are present in this structure, while highlighting the capabilities of the proposed method. The examples are generated from experimental data of simulated acoustic emission events. The data corresponds to helical guided ultrasonic waves generated in a 3 m long large diameter pipe by pencil lead breaks on its outer surface. Acoustic emission waveforms are recorded by six sparsely distributed low-profile piezoelectric transducers instrumented on the outer surface of the pipe. The same array of transducers is used for both the proposed and the triangulation method. It is demonstrated that the proposed method is able to localize multiple events occurring closely in time. Furthermore, the matching pursuit algorithm and the basis pursuit densoising approach are each evaluated as potential numerical tools in the proposed sparse reconstruction method.

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose.

Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Investigated is the ability of ultrasonic guided waves to detect flaws and assess the quality of friction stir welds (FSW). AZ31B magnesium plates were friction stir welded. While process parameters of spindle speed and tool feed were fixed, shoulder penetration depth was varied resulting in welds of varying quality.

Ultrasonic waves were excited at different frequencies using piezoelectric wafers and the fundamental symmetric (S0) mode was selected to detect the flaws resulting from the welding process. The front of the first transmitted wave signal was used to capture the S0 mode. A damage index (DI) measure was defined based on the amplitude attenuation after wave interaction with the welded zone. Computed Tomography (CT) scanning was employed as a nondestructive testing (NDT) technique to assess the actual weld quality. Derived DI values were plotted against CT-derived flaw volume resulting in a perfectly linear fit. The proposed approach showed high sensitivity of the S0 mode to internal flaws within the weld. As such, this methodology bears great potential as a future predictive method for the evaluation of FSW weld quality.

This paper presents a structural damage identification approach based on the analysis of the data from a hybrid network of self-powered accelerometer and strain sensors. Numerical and experimental studies are conducted on a plate with bolted connections to verify the method. Piezoelectric ceramic Lead Zirconate Titanate (PZT)-5A ceramic discs and PZT-5H bimorph accelerometers are placed on the surface of the plate to measure the voltage changes due to damage progression. Damage is defined by loosening or removing one bolt at a time from the plate. The results show that the PZT accelerometers provide a fairly more consistent behavior than the PZT strain sensors. While some of the PZT strain sensors are not sensitive to the changes of the boundary condition, the bimorph accelerometers capture the mode changes from undamaged to missing bolt conditions. The results corresponding to the strain sensors are better indicator to the location of damage compared to the accelerometers. The characteristics of the overall structure can be monitored with even one accelerometer. On the other hand, several PZT strain sensors might be needed to localize the damage.

In the last decades the interest in the development of sensors for low-frequency long-term monitoring systems aimed to assess the structural health status of large buildings and infrastructures like dams, bridges, sky-scrapes, oil platforms, etc., has largely increased. Among the different architecture of candidate sensors for this task, the UNISA Folded Pendulum class of sensors is one of the most promising ones for the implementation of mechanical accelerometers (horizontal, vertical and angular). In this paper, we present monolithic implementations of inertial mechanical seismometers/accelerometers based on the UNISA Folded Pendulum mechanical configuration, optimized for low frequency characterization of large structures.

In this study, the hardening process of ultra high performance concrete (UHPC) was monitored non-destructively using a single embedded sensor system and the characteristics of guided waves, especially the Lamb wave. Lamb wave propagation depends on the material properties of the medium and boundary conditions. Since the boundary conditions of the embedded sensor system continuously change during the hardening process of concrete materials, the measured characteristics of the propagating waves also vary. To understand the variations in wave propagation, the Lamb modes were decomposed using the polarization characteristics of piezoelectric sensors, which were used to measure wave responses. Additionally, a traditional penetration resistance method was adopted to estimate the time for phase transition of UHPC. The decomposed Lamb modes were compared to measurements of penetration resistance. The strength development of UHPC, with and without short-fiber reinforcement, was estimated using the variation of patterns of the decomposed Lamb modes after the phase transition. Based on the proposed methodology, which measures the propagation and variation of the Lamb waves, it is possible to estimate the time of phase transition and the strength development of UHPC.

In the present work, a reliable and low cost system has been designed and implemented to measure greenhouse gases (GHG) in United Arab Emirates (UAE) by using unmanned aerial vehicle (UAV). A set of accurate gas, temperature, pressure, humidity sensors are integrated together with a wireless communication system on a microcontroller based platform to continuously measure the required data. The system instantaneously sends the measured data to a center monitoring unit via the wireless communication system. In addition, the proposed system has the features that all measurements are recorded directly in a storage device to allow effective monitoring in regions with weak or no wireless coverage. The obtained data will be used in all further sophisticated calculations for environmental research and monitoring purposes.

Structural health monitoring assesses structural integrity by processing the measured responses of structures. One particular group in the structural health monitoring research is to conduct the operational modal analysis and then to extract the dynamic characteristics of structures from vibrational responses. These characteristics include natural frequencies, damping ratios, and mode shapes. Deviations in these characteristics represent the changes in structural properties and also imply possible damage to structures. In this study, a new stochastic system identification is developed using multivariate time-frequency distributions. These time-frequency distributions are derived from the short-time Fourier transform and subsequently yield a time-frequency matrix by stacking them with respect to time. As the derivation in the data-driven stochastic subspace system identification, the future time-frequency matrix is projected onto the past time-frequency matrix. By exploiting the singular value decomposition, the system and measurement matrices of a stochastic state-space representation are derived. Consequently, the dynamic characteristics of a structure are obtained. As compared to the time-domain stochastic subspace system identification, the proposed method utilizes the past and future matrices with a lower dimension in projection. A spectral magnitude envelope can be applied to the time-frequency matrix to highlight the major frequency components as well as to eliminate the components with less influence. To validate the proposed method, a numerical example is developed. This method is also applied to experimental data in order to evaluate its effectiveness. As a result, performance of the proposed method is superior to the time-domain stochastic subspace system identification.

Structural health monitoring (SHM) is considered as an incentive multi-disciplinary technology for conditional assessment of infrastructure system. However, most classical output-only system identification methods based on a stationary assumption fail to achieve reliable results under non-stationary scenarios. Numerous techniques from the disciplines of multivariate statistics and pattern recognition in the field of structural damage detection have been developed, such as stochastic subspace identification or null-space and subspace damage identification. In order to obtain the accurate and quick structural damage assessment of a structure, measurement from all sensing nodes in the structure must be used. Therefore, dimension reduction and data visualization techniques for damage assessment need to be investigated. In this study a structural health monitoring method for damage identification and localization, which incorporated with the principal component analysis (PCA) based data compression and pattern recognition is developed. First, the frequency response function (FRF)-based damage assessment will be investigated. Based on the FRF to construct the Sammon map from all the measurements will be investigated. Sammon’s mapping is a nonlinear dimensional reduction algorithm which seeks to preserve distances as far as possible. Similarity among different data structure can be used to detect damage localization. Besides, the correlation of time-frequency analysis of data (Hilbert amplitude spectrum from WPT) among different measurement and the differences on the reconstructed data using multivariate AR-model are also used to detection damage. Verification of the proposed methods by using shaking table tests of two structures is demonstrated: One is focus on the damage of lower vibration modes and the other one is focus on the damage of high frequency modes.

This study explores the use of fractional differential equations to model the vibration of single (SDOF) and multiple degree of freedom (MDOF) discrete parameter systems. In particular, we explore methodologies to simulate the dynamic response of discrete systems having non-uniform coefficients (that is, distribution of mass, damping, and stiffness) by using fractional order models. Transfer functions are used to convert a traditional integer order model into a fractional order model able to match, often times exactly, the dynamic response of the active degree in the initial integer order system. Analytical and numerical results show that, under certain conditions, an exact match is possible and the resulting differential models have both frequency-dependent and complex fractional order. The presented methodology is practically equivalent to a model order reduction technique that is able to match the response of non-uniform MDOF systems to a simple fractional single degree of freedom (F-SDOF) systems. The implications of this type of modeling approach will be discussed.

This paper describes a cloud-based cyberinfrastructure framework for the management of the diverse data involved in bridge monitoring. Bridge monitoring involves various hardware systems, software tools and laborious activities that include, for examples, a structural health monitoring (SHM), sensor network, engineering analysis programs and visual inspection. Very often, these monitoring systems, tools and activities are not coordinated, and the collected information are not shared. A well-designed integrated data management framework can support the effective use of the data and, thereby, enhance bridge management and maintenance operations. The cloud-based cyberinfrastructure framework presented herein is designed to manage not only sensor measurement data acquired from the SHM system, but also other relevant information, such as bridge engineering model and traffic videos, in an integrated manner. For the scalability and flexibility, cloud computing services and distributed database systems are employed. The information stored can be accessed through standard web interfaces. For demonstration, the cyberinfrastructure system is implemented for the monitoring of the bridges located along the I-275 Corridor in the state of Michigan.

With the development of photoacoustic technology in recent years, ultrasound-related sensors play a vital role in a number of areas ranging from scientific research to nondestructive testing. Compared with the traditional PZT transducer as ultrasonic sensors, novel ultrasonic sensors based on optical methods such as micro-ring resonators have gained increasing attention. The total internal reflection of the light along the cavity results in light propagating in microcavities as whispering gallery modes (WGMs), which are extremely sensitive to change in the radius and refractive index of the cavity induced by ultrasound strain field. In this work, we present a polymer optical micro-ring resonator based ultrasonic sensor fabricated by direct laser writing optical lithography. The design consists of a single micro-ring and a straight tapered waveguide that can be directly coupled by single mode fibers (SMFs). The design and fabrication of the printed polymer resonator have been optimized to provide broad bandwidth and high optical quality factor to ensure high detection sensitivity. The experiments demonstrate the potential of the polymer micro-ring resonator to works as a high-performance ultrasonic sensor.

This paper describes a new method for the classification and identification of two major types of defects in composites, namely delamination and matrix cracks, by classification of the spectral features of fibre Bragg grating (FBG) signals. In aeronautical applications of composites, after a damage is detected, it is very useful to know the type of damage prior to determining the treatment method of the area or perhaps replacing the part. This was achieved by embedding FBG sensors inside a glass-fibre composite, and analysing the output signal from the sensors. The glass-fibre coupons were subjected to mode-I loading under tension-compression and static tests, in order to induce matrix cracks and delamination damages respectively. Afterwards, using wavelet features extracted from spectral measurements of the FBG sensors, classification of the damage type was carried out by means of support vector machines as a general classification tool with a quadratic kernel.

Critical non-structural equipment, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., is vulnerable to strong earthquakes, and on top of that, the failure of the vibration-sensitive equipment will cause severe economic loss. In order to protect vibration-sensitive equipment or machinery against strong earthquakes, various innovative control algorithms are developed to compensate the internal forces that to be applied. These new or improved control strategies, such as the control algorithms based on optimal control theory and sliding mode control (SMC), are also developed for structures engineering as a key element in smart structure technology. The optimal control theory, one of the most common methodologies in feedback control, finds control forces through achieving a certain optimal criterion by minimizing a cost function. For example, the linear-quadratic regulator (LQR) was the most popular control algorithm over the past three decades, and a number of modifications have been proposed to increase the efficiency of classical LQR algorithm. However, except to the advantage of simplicity and ease of implementation, LQR are susceptible to parameter uncertainty and modeling error due to complex nature of civil structures. Different from LQR control, a robust and easy to be implemented control algorithm, SMC has also been studied. SMC is a nonlinear control methodology that forces the structural system to slide along surfaces or boundaries; hence this control algorithm is naturally robust with respect to parametric uncertainties of a structure. Early attempts at protecting vibration-sensitive equipment were based on the use of existing control algorithms as described above. However, in recent years, researchers have tried to renew the existing control algorithms or developing a new control algorithm to adapt the complex nature of civil structures which include the control of both structures and non-structural components.

The aim of this paper is to develop a hybrid control algorithm on the control of both structures and equipments simultaneously to overcome the limitations of classical feedback control through combining the advantage of classic LQR and SMC. To suppress vibrations with the frequency contents of strong earthquakes differing from the natural frequencies of civil structures, the hybrid control algorithms integrated with the wavelet-base vibration control algorithm is developed. The performance of classical, hybrid, and wavelet-based hybrid control algorithms as well as the responses of structure and non-structural components are evaluated and discussed through numerical simulation in this study.

Polymer film-type slip sensor is presented by using novel working principle rather than measuring micro-vibration. The sensor is comprised of bilayer with Ecoflex and NBR(acrylonitrile butadiene rubber) films divided by di-electric. When slip occur on surface, bilayer have relative displacement from each other because friction-induced vibration make a clearance between two layers. This displacement can be obtained by capacitance difference. CNT(carbon nanotube) was employed for electrode because of flexible and stretchable characteristics. Also normal and shear force can be decoupled by the working principle. To verify developed sensor, slip test apparatus was designed and experiments were conducted.

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

As the covering of morphing wings, flexible skin is required to provide adequate cooperation deformation, keep the smoothness of the aerodynamic configuration and bear the air load. The non-deformation direction of flexible skin is required to be restrained to keep the smoothness during morphing. This paper studies the deformation mechanisms of a cruciform honeycomb under zero Poisson’s ratio constraint. The morphing capacity and in-plane modulus of the cruciform honeycomb are improved by optimizing the shape parameters of honeycomb unit. To improve the out-of-plane bending capacity, a zero Poisson’s ratio mixed cruciform honeycomb is proposed by adding ribs into cruciform honeycomb, which can be used as filling material of flexible skin. The mechanical properties of the mixed honeycomb are studied by theoretical analysis and simulation. The local deformation of flexible skin under air load is also analyzed. Targeting the situation of non-uniform air load, a gradient density design scheme is referred. According to the design requirements of the variable camber trailing edge wing flexible skin, the specific design parameters and performance parameters of the skin based on the mixed honeycomb are given. The results show that the zero Poisson’s ratio mixed cruciform honeycomb has a large bending rigidity itself and can have a better deformation capacity in-plane and a larger bending rigidity out-of-plane by optimizing the shape parameters. Besides, the designed skin also has advantages in driving force, deformation capacity and quality compared with conventional skin.

This paper considers an ultrasonic transducer composed of piezoelectric solid and hollow discs. The inner solid disc is a transmitter and the outer hollow disc is a receiver of ultrasound. The purpose of this work is to design a disc-shape transducer with separate transmitter and receiver which are operated at a same frequency and have a same center location. The vibration characteristics of the radial and axial motions in the axisymmetric modes were investigated theoretically and experimentally. Theoretically, the differential equations of piezoelectric motions were derived in terms of mechanical displacements and electric potential, and they were solved to produce characteristic equations providing natural frequencies and mode shapes. The theoretical analysis was complimented by the finite-element analysis, which provided three-dimensional mode shapes. Experimentally, the natural frequencies and the radial in-plane motion were measured using an impedance analyzer and an in-plane laser interferometer, respectively. The results of the theoretical analysis were compared with those of experiments, and the theoretical analysis was verified on the basis of this comparison. It appeared that the fundamental frequency of the transducer was similar to but slightly different from that of a single disc transducer of same size. It was found that the transducer composed of inner solid and outer hollow piezoelectric discs could be designed by selecting suitable diameters of each disc.

According to the situation of oil leakage and the development of oil detection technology, a wireless monitoring system, combining with the sensor technology, optical measurement technology, and wireless technology, is designed. In this paper, the architecture of a wireless system is designed. In the hardware, the collected data, acquired by photoelectric conversion and analog to digital conversion equipment, will be sent to the upper machine where they are saved and analyzed. The experimental results reveals that the wireless system has the characteristics of higher precision, more real-time and more convenient installation, it can reflect the condition of the measuring object truly and implement the dynamic monitoring for a long time on-site, stability—thus it has a good application prospect in the oil monitoring filed.

Large bridges play a significant role in the development of both the urban traffic condition and the social economy. It is of high importance to monitor the operational bridges and to assess their security from the perspective of people’s life and property safety. In this paper, a wireless bridge structure monitoring system was developed and DMTS synchronization algorithm (based on the one-way synchronization mechanism of the sender) which can meet the system requirement was proposed. Then the deck vibration test of a bridge in Xiamen was carried out. The study shows that the wireless sensing system has the advantage of high accuracy, and the feature of easy operation, good instantaneity, and low overhead costs, which has a good application prospect in the field of structure monitoring and condition assessment of the bridges.

Recently, the development of high-speed railway meets the requirement of society booming and it has gradually become the first choice for long-length journey. Since ensuring the safety and stable operation are of great importance to high-speed trains owing to its unique features, vibration analysis checking is one of main means to be adopted. Due to the popularization of Smartphone, in this research, a novel public-participating method to achieve high-speed railway’s vibration analysis checking based on smartphone and an inspection application of high-speed railway line built in the intelligent mobile terminal were proposed. Utilizing the accelerometer, gyroscope, GPS and other high-performance sensors which were integrated in smartphone, the application can obtain multiple parameters like acceleration, angle, etc and pinpoint the location. Therefore, through analyzing the acceleration data in time domain and frequency domain using fast Fourier transform, the research compared much of data from monitoring tests under different measure conditions and measuring points. Furthermore, an idea of establishing a system about analysis checking was outlined in paper. It has been validated that the smartphone-based high-speed railway line inspection system is reliable and feasible on the high-speed railway lines. And it has more advantages, such as convenience, low cost and being widely used. Obviously, the research has important practical significance and broad application prospects.

With the rapid development of high-rise buildings, the requirement of the elevator’s speed is growing higher. And the vibration amplitude of elevator will also increasing with the improvement of running speed. The vibration problems of elevator have become the important factors that affect the comfort feeling of elevator. At the same time, the strong vibration will affect the normal work of elevator, and even cause accidents. So it's necessary to study the vibration characteristics of the elevator. In recent years, smartphone has developed rapidly, with a variety of sophisticated sensors; it has the powerful data processing and transmission capacity. In this paper, the author has presented an elevator comfort monitoring method based on smartphone. This method using Monitoring App can monitor the acceleration and inclination information using MEMS sensors embedded in smartphone. Then a confirmatory test for an elevator was designed, experimental results show that elevator comfort monitoring method based on smartphone is stable and reliable.

Macro-Fiber Composites (MFCs) are a piezoelectric material typically employed in applications ranging from vibration damping to actuation to structural health monitoring. These composites have flown in space but only with thermal protection and for a short duration. They have not been significantly tested under thermally cyclic conditions similar to those they would experience in Low-Earth Orbit (LEO) without shielding. Research has shown that the performance of MFCs varies when the MFC undergoes a thermal cycle. This paper outlines an autonomous experiment that will be able to run impedance measurements and actuate MFCs, further testing their performance in a space environment where thermal cycles are common. This will be installed on a CubeSat and flown to LEO where it will collect data and downlink it back for study. Details of the layout of the experiments and electronic systems being used on the CubeSat for the payload are presented alongside future steps that need to be taken to ensure a successful flight.

Reducing vibration of high-rise structures under earthquake load has been the subject of considerable efforts in Japan. Relevant researches about vibration energy dissipation devices for buildings have been undertaken. An active mass damper is one of the well-known vibration control devices. Despite the accumulation of much knowledge of control design methods for the system, application of the devices to high-rise structures under earthquake load is challenging, because the active mass dampers have one serious disadvantage about stroke limitation of the auxiliary mass. In this study, we have proposed a new control system, which had a neural oscillator and position controller, to solve this problem. The objective of this paper is to improve the vibration control performance of the proposed active mass damper system. The previous method generated rectangular waves as the desired displacement, whose amplitude is varied in accordance with the vibration responses of a structure excited by earthquakes. Furthermore, the gains of the position controller, which derives the auxiliary mass to the desired displacement, have been designed in consideration of response reduction of the structure. However, the generated rectangular desired displacement was not adequate to reduce the maximum acceleration responses of the structure, because the driving force for the auxiliary mass generates excessive amounts of acceleration as the direction of the desired displacement is switched. Thus, this paper proposes a new method, which generates sinusoidal varying desired displacement for the auxiliary mass of the active mass damper system to reduce the acceleration response of structures. The results of numerical simulation showed that the proposed method in this work was effective for improving the control performance.

Health monitoring methods for machines have been the subject of considerable efforts to maintain it at an appropriate timing. Failures of rotating machine elements can cause severe accidents, thus, to detect such failures is an important issue. However, health monitoring of rotating machine elements, such as gears, is challenging because of rotation at high speed in gearboxes, geometric complexity, space limitation for measurements, or another operation conditions. The long-term objective of the present research is to develop smart sensor systems for detecting gear failure signs. As the very first step, this paper proposes a new method to manufacture electrical circuits, such as sensors or antennas, on gears. We print these circuits directly on the gear surface using a laser sintering technique of conductive ink. For this purpose, we have begun to develop a 4-axis laser printing system. This paper shows the laser sintering conditions of the conductive ink splayed on steel plates insulated by polyimide layers. The conductivity of the printed lines was evaluated through observation with a miniature scanning electron microscope. Finally, according to the obtained laser sintering conditions, a meander line antenna was printed as a part of smart sensor systems.

Part-to-part variability and poor part quality due to failure to maintain geometric specifications pose a challenge for adopting Additive Manufacturing (AM) as a viable manufacturing process. In recent years, In-process Monitoring and Control (InPMC) has received a lot of attention as an approach to overcome these obstacles. The ability to sense geometry of the deposited layers accurately enables effective process monitoring and control of AM application. This paper demonstrates an application of geometry sensing technique for the coating deposition Cold Spray process, where solid powders are accelerated through a nozzle, collides with the substrate and adheres to it. Often the deposited surface has shape irregularities. This paper proposes an approach to suppress the iregularities by controlling the deposition height. An analytical control-oriented model is developed that expresses the resulting height of deposit as an integral function of nozzle velocity and angle. In order to obtain height information at each layer, a Micro-Epsilon laser line scanner was used for surface profiling after each deposition. This surface profile information, specifically the layer height, was then fed back to an optimal control algorithm which manipulated the nozzle speed to control the layer height to a pre specified height. While the problem is heavily nonlinear, we were able to transform it into equivalent Optimal Control problem linear w.r.t. input. That enabled development of two solution methods: one is fast and approximate, while another is more accurate but still efficient.

Many modern industrialized systems such as aircrafts, rotating turbines, satellite booms, etc. cannot perform their desired tasks accurately if their uninhibited structural vibrations are not controlled properly. Structural health monitoring and online reliability calculations are emerging new means to handle system imposed uncertainties. As stochastic forcing are unavoidable, in most engineering systems, it is often needed to take them into the account for the control design process. In this research, smart material technology is utilized for structural health monitoring and control in order to keep the system in a reliable performance range. In this regard, a reliability-based cost function is assigned for both controller gain optimization as well as sensor placement. The proposed scheme is implemented and verified for a wing section. Comparison of results for the frequency responses is considered to show potential applicability of the presented technique.

This paper describes the development and testing of Metal Rubber sensors for the nondestructive, normal force detection of ice accretion on aerospace structures. The buildup of ice on aircraft engine components, wings and rotorblades is a problem for both civilian and military aircraft that must operate under all weather conditions. Ice adds mass to moving components, thus changing the equations of motion that control the operation of the system as well as increasing drag and torque requirements. Ice also alters the surface geometry of leading edges, altering the airflow transition from laminar to turbulent, generating turbulence and again increasing drag. Metal Rubber is a piezoresistive material that exhibits a change in electrical resistance in response to physical deformation. It is produced as a freestanding sheet that is assembled at the molecular level using alternating layers of conductive metal nanoparticles and polymers. As the volume percentage of the conductive nanoparticle clusters within the material is increased from zero, the onset of electrical conduction occurs abruptly at the percolation threshold. Electrical conduction occurs due to electron hopping between the clusters. If a length of the material is strained, the clusters move apart so the efficiency of electron hopping decreases and electrical resistance increases. The resulting change in resistance as a function of the change in strain in the material, at a specific volume percentage of conductive clusters, can be interpreted as the transduction response of the material. We describe how sensors fabricated from these materials can be used to measure ice buildup.

In this paper we describe the characteristics and performances of a monolithic sensor designed for low frequency motion measurement of spacecrafts and satellites, whose mechanics is based on the UNISA Folded Pendulum. The latter, developed for ground-based applications, exhibits unique features (compactness, lightness, scalability, low resonance frequency and high quality factor), consequence of the action of the gravitational force on its inertial mass. In this paper we introduce and discuss the general methodology used to extend the application of ground-based folded pendulums to space, also in total absence of gravity, still keeping all their peculiar features and characteristics.

This paper presents the development of an ultrasonic guided Lamb wave (GLW)-based magnetostrictive phased array sensor (MPAS) using a circular comb-shaped nickel disc patch with 1” in diameter. And its damage detection capability to identify loosened joint bolts is experimentally demonstrated. The compact sized MPAS was comprised of the nickel disc patch and a detachable magnetic circuit device. The disc patch was machined with 24 comb fingers along its radial direction and the magnetic circuit device contained 6 sensing coils and cylindrical biasing magnets. The individual sensing coils appear to have distinct directional sensing preferences designated by the normal direction of coil winding. The directional sensing feature of the developed MPAS is offered by the combined effect of the magnetic shape anisotropy of comb finger formation in the nickel patch and the sensing directionality of the coil sensor. The MPAS detects the strain-induced magnetic property change on the nickel comb patch due to the mechanical interaction between the patch and GLWs. Although the MPAS holds only the 6 physical coil sensors, the array sensor enables to acquire additional GLW signal data from different sensing sections within the nickel patch, by simply altering the rotational orientation of the magnetic circuit device. Such signal data additions allow to provide a higher resolution damage detection scheme for the advanced phased array signal processing technique. The MPAS apparatus and its damage detection capability were experimentally validated by GLW inspection testing with a thin aluminum plate installed with numerous joint bolts.

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes’ Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

Distributed sensors based on phase-optical time-domain reflectometry (phase-OTDR) are suitable for aircraft health monitoring due to electromagnetic interference immunity, small dimensions, low weight and flexibility. These features allow the fiber embedment into aircraft structures in a nearly non-intrusive way to measure vibrations along its length. The capability of measuring vibrations on avionics structures is of interest for what concerns the study of material fatigue or the occurrence of undesirable phenomena like flutter. In this work, we employed the phase-OTDR technique to measure vibrations ranging from some dozens of Hz to kHz in two layers of composite material board with embedded polyimide coating 0.24 numerical aperture single-mode optical fiber.

The problem of estimating the location of an impact force in a dispersive medium is complicated given the dispersion-related distortion of the generated traveling wave. The problem cannot be solved, with reasonable accuracy, using conventional time difference of arrival (TDOA) techniques. A building floor is an example of a dispersive medium that is being loaded by occupant footsteps. If more accurate localization algorithms are obtained, then they can be used to localize and track occupants in a building using floor vibration sensors measuring the footstep-induced traveling waves. This paper presents the evaluation of a new localization approach, in a simulated aluminum plate (dispersive waveguide), using a network of sensors measuring the plate's vibration. Average signal power is calculated for all the sensors over a fixed time period, and then used to generate a location estimate. Two different location estimation solutions are presented and compared; a constrained least squares solution (CLS), and a non-linear root finding solution generated using the Levenberg-Marquardt (LM) algorithm. A finite element (FE) thin plate model is used as a testbed to evaluate the performance of the developed localization algorithm by estimating the location of virtual hammer impacts acting on the plate. The results encourage further future development.

A fast crack profile reconstitution method is developed using eddy current probing scheme. The technique employed genetic algorithm (GA) optimization together with boundary element method (BEM) to evaluate the shape of the cracks that are described by using the Lagrange polynomial (LP). In doing so, the shape of the open cracks could be obtained. Compared to the pixel-based description of the cracks, the LP description could significantly reduce the number of the unknowns, and this increases the efficiency of the global searching by the GA. This technique has greatly improved the efficiency of the inversion solver. Numerical tests are shown to validate the interests.