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Structural Engineering and Mechanics Volume 71, Number 1, July10 2019 , pages 57-63 DOI: https://doi.org/10.12989/sem.2019.71.1.057 |
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Application of power spectral density function for damage diagnosis of bridge piers |
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Mahmoud Bayat, Hamid Reza Ahmadi and Navideh Mahdavi
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| Abstract | ||
| During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge\'s piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers\' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge\'s piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models. | ||
| Key Words | ||
| moment-rotation; forecasting; extreme learning machine; precast beam-to-column connection; partly hidden corbel | ||
| Address | ||
| Mahmoud Bayat: Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran Hamid Reza Ahmadi: Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, P.O. Box 55136-553, Iran Navideh Mahdavi: Department of Civil Engineering, Marand Branch, Islamic Azad University, Marand, Iran | ||