Pedestrian-Induced Load Identification from Structural Responses Using Genetic Algorithm with Numerical and Experimental Validation
Publication: Journal of Bridge Engineering
Volume 26, Issue 3
Abstract
A reliable pedestrian load model is a prerequisite for the accurate calculation of human-induced structural vibration. In recent years, many researchers have proposed pedestrian load models based on direct force measurements, such as force plates. However, direct measurement techniques often suffer from incapability when applied to real structures in operation and can hardly be used to measure crowd loads. In this paper, an inverse load identification method is proposed to extract pedestrian vertical load from structural responses. Through a genetic algorithm, the pedestrian’s pacing frequency, dynamic load factors, and phase angles in the Fourier-series model are identified from structural acceleration responses. The proposed algorithm is further investigated for the identification of multiple-pedestrian load parameters, where structural displacement responses are used to give an equivalent number of pedestrians. Numerical examples demonstrate that the pedestrian load parameters are estimated with high accuracy and robustness against noise and modeling errors. A sensitivity analysis is given to explain the different estimation accuracies among the parameters. Finally, the proposed method is validated through an experimental test, showing its practicality for identifying pedestrian loads in real structures.
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Acknowledgments
The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (U1711264 and 51778465), the State Key Laboratory for Disaster Reduction of Civil Engineering (SLDRCE19-B-22), and the Shanghai Sailing Project (20YF1451300).
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Published In
Journal of Bridge Engineering
Volume 26 • Issue 3 • March 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 8, 2020
Accepted: Oct 1, 2020
Published online: Jan 6, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 6, 2021
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