Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

Abstract

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.