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a b s t r a c tMonitoring 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.

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Abstract-In this paper we describe our custom designed, lowpower, intelligent sensor platform, and a novel analysis approach for Structural Health Monitoring (SHM). More specifically we show how Acoustic Emission (AE) signals were recorded during aluminium and steel beam break tests utilizing two channels on our MarmotE platform, and how subsequent low-resource but accurate onset time detection yielded Time Difference of Arrival (TDoA) results. We also demonstrate a new, simplified method to pick valid AE events from a vast set of noisy measurements, and prove the feasibility of our ideas by showing that our approach provided results comparable to widely used industry methods with modest resource requirements.