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Instrumentation plan verification for damage detection of a vertical lift steel truss bridge
Author(s): Milad Mehrkash; Vahid Shahsavari; Erin Santini-Bell
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Paper Abstract

Structural condition assessment of critical infrastructures involves smart instrumentation by a suitable grid of sensors. As instrumenting of large-scale civil structures is costly, design of optimal instrumentation plans that reflect the structural behavior of the bridges while minimizing the project costs is a crucial issue in their health monitoring. An optimal instrumentation in terms of type and number of sensors with their distribution pattern throughout the bridge depends on the structural behavior of interest. The sensitivity of instrumentation plan to different performance measures is dependent to the type and placement of sensors. Therefore, the effectiveness of a damage detection algorithm could be significantly impacted by the sensor layout. In this study, the dynamic instrumentation of a vertical lift steel gusset-less truss bridge is investigated. Several damage scenarios are simulated in its analytical model while various acceleration measurement locations of one of the fixed spans are considered in each case. The vibration of the bridge fixed span used in this study is due to the vertical impact of the lift span on the piers. The damage detection methodology relies on the application of the wavelet theory to demonstrate the structural condition of the bridge based on the predicted accelerations of the structure. As the current instrumentation layout of the bridge has been designed mainly for model updating and fatigue assessment of its gussetless connections, this paper provides some guidelines for modifications of the sensor configuration for future condition assessment and health monitoring purposes of the bridge.

Paper Details

Date Published: 27 March 2019
PDF: 13 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109700Q (27 March 2019); doi: 10.1117/12.2514338
Show Author Affiliations
Milad Mehrkash, The Univ. of New Hampshire (United States)
Vahid Shahsavari, The Univ. of New Hampshire (United States)
Erin Santini-Bell, The Univ. of New Hampshire (United States)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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