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Proceedings Paper

Gaussian mixture modeling of acoustic emissions for structural health monitoring of reinforced concrete structures
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Paper Abstract

Reinforced Concrete (RC) has been widely used in construction of infrastructures for many decades. The cracking behavior in concrete is crucial due to the harmful effects on structural performance such as serviceability and durability requirements. In general, in loading such structures until failure, tensile cracks develop at the initial stages of loading, while shear cracks dominate later. Therefore, monitoring the cracking modes is of paramount importance as it can lead to the prediction of the structural performance. In the past two decades, significant efforts have been made toward the development of automated structural health monitoring (SHM) systems. Among them, a technique that shows promises for monitoring RC structures is the acoustic emission (AE). This paper introduces a novel probabilistic approach based on Gaussian Mixture Modeling (GMM) to classify AE signals related to each crack mode. The system provides an early warning by recognizing nucleation of numerous critical shear cracks. The algorithm is validated through an experimental study on a full-scale reinforced concrete shear wall subjected to a reversed cyclic loading. A modified conventional classification scheme and a new criterion for crack classification are also proposed.

Paper Details

Date Published: 19 April 2013
PDF: 10 pages
Proc. SPIE 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, 86920B (19 April 2013); doi: 10.1117/12.2008705
Show Author Affiliations
Alireza Farhidzadeh, The Univ. at Buffalo (United States)
Ehsan Dehghan-Niri, The Univ. at Buffalo (United States)
Salvatore Salamone, The Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 8692:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
Jerome Peter Lynch; Chung-Bang Yun; Kon-Well Wang, Editor(s)

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