Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations

Abstract

In structural engineering, damage detection in concrete gravity dams (CGDS) is a practical problem. Dam destruction can have severe financial consequences and may even lead to fatalities. Therefore, structural health monitoring in advance is crucial. In this regard, a well-known CGD, namely the Pine Flat Dam, has been chosen for the Finite Element Modeling. In this paper, damage is induced in the dam neck through elasticity modulus reduction by 40 % and 80 %. In addition, after applying Northridge earthquake, the acceleration in structure nodes for intact and damaged cases are recorded in vector formats. Using various methods, such as Discrete-time Fourier Transform (DTFT), Wavelet transform and Wiener transform, the differences between these two signals are investigated. The standard deviation (S.D.) of variations is chosen as the performance metric and is applied to the signal amplitude between intact and damage observations/signals. The reason why several signal processing algorithms are used is finding an approach which shows more clearly the differences caused by the destruction. This is evaluated via S.D. values for different algorithms. The results confirm the superiority of DTFT over other given algorithms. DTFT has a negligible outperformance (approximately zero dB) with respect to the Wavelet transform in both the crest and the lower nodes of the dam. This rate for DTFT and Wavelet is 10dB higher than that of Wiener and 35 dB in comparison with the simple amplitude difference. Moreover, the detection thresholds for the given methods are compared, and it is verified that the DTFT and Wavelet indicate the best performance.

Blended Teaching for Signal and System Course Based on Internet-Engineering Education

“…With the development of information science and computer science, signal processing technology has developed rapidly and presented endless prospects. It is widely used in the fields of speech recognition, communication, robot vision sensing, earthquake, event-related potential (ERP) technology, industrial monitoring, and so on [1][2][3][4][5][6]. Signal and system course (SSC) provide basic theories and methods for the application of signal and system (SS) processing technology in these fields.…”