AE Based Health Monitoring and Bonded FRP Patch Repair in Bridge Management
For effective bridge management, monitoring its health and repairing the detected damages efficiently are essential. The first part of this study uses acoustic emission (AE) technique for real-time health monitoring. For this purpose, AE sensor instrumented representative structural elements of steel, reinforced concrete, and prestressed concrete are tested under cyclic loading. In processing the recorded signals, suitable wavelet, Fourier transform and artificial neural network (ANN) based schemes were developed to de-noise, locate and identify the damage sources. The health monitoring system was simulated using nonlinear finite element method and verified with experimental investigations. The developed finite element scheme proved to be a good method to train the ANN in real-life situations. Second part of this study is concerned with restoring bridge capacity by efficient repair of damages identified during health monitoring using bonded FRP patch of four different types. The performance of the repaired bridge components was investigated by laboratory experimentation and simulation using newly developed schemes based on mechanistic and finite element modeling. The research achieved significant success in (a) developing an AE signal processing scheme irrespective of bridge material; (b) repairing for locally damaged beams using bonded FRP patch; (c) developing a highly realistic finite element modeling scheme for pretensioned concrete structures; (d) developing a finite element modeling scheme for AE based health monitoring; (e) formulating a mechanistic model for quick prediction of performance of flexural patch repaired beams; and (f) demonstrating the success of the localized patch repair in restoring the fatigue life.