Structural health monitoring of bridges with piezoelectric AE sensors. (October 2015)

• Record Type: Journal Article
• Authors: Yapar, O.; Basu, P.K.; Volgyesi, P.; Ledeczi, A.
• Is Part Of: Engineering failure analysis. Volume 56(2015:Oct.)
• Page Start: 150
• Page End: 169
• Publication Date: 2015-10
• Subjects: Acoustic emission -- Bridge failures -- Crack growth -- Damage assessment -- Non-destructive testing; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2015.03.009
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Acoustic emission signal noise is successfully eliminated by using wavelet transformation. By training an artificial neural network, source location of real acoustic emission events is predicted with high accuracy. Power spectrum FFT and CWT enabled successful source characterization of AE signals. Abstract: Monitoring the health of structures using acoustic emission technique offers many advantages such as early and quick detection of damage in real-time inexpensively. This paper is concerned with health monitoring of infrastructure hardware like highway bridges using this technique for timely intervention preventing catastrophic failure. Both experimental and numerical investigations were undertaken to determine the effectiveness and applicability of the method in the case of bridge superstructures. Three types of representative bridge girders of steel, reinforced concrete and prestressed concrete were designed, fabricated and monitored in the laboratory for structural integrity under cyclic loads with the help of an acoustic emission sensor system. After laboratory work, wavelet and Fourier transform techniques were applied to the recorded signals for de-noising and to diagnose the damaged state of the structure. Finally the locations of the cracks were determined by using the artificial neural network (ANN) approach.