Signal Identification of Wire Breaking in Bridge Cables Based on Machine Learning

Guangming Li, Heming Ding, Yaohan Li, Chun-Yin Li () and Chi-Chung Lee
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Guangming Li: Department of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Heming Ding: Department of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Yaohan Li: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Chun-Yin Li: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Chi-Chung Lee: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

Mathematics, 2022, vol. 10, issue 19, 1-17

Abstract: With the booming development of bridge construction, bridge operation and maintenance have always been major issues to ensure the safety of the community. Affected by the long-term service of bridges and natural factors, the safety and durability of cables can be threatened. Cables are critical stress-bearing elements of large bridges such as cable-stayed bridges. Realizing the health monitoring of bridge cables is the key to ensuring the normal operation of bridges. Acoustic emission (AE) is a dynamic nondestructive testing method that is increasingly used in the local monitoring of bridge cables. In this paper, a testbed is described for generating the acoustic emission signals for signal identification testing with machine learning (ML) models. Owing to the limited number of measured signals being available, an algorithm is proposed to simulate acoustic emission signals for model training. A multi-angle feature extraction method is proposed to extract the acoustic emission signals and construct a comprehensive feature vector to characterize the acoustic emission signals. Seven ML models are trained with the simulated acoustic emission signals. Long short-term memory (LSTM) has been specially applied for deep learning demonstration which requires a large amount of training data. As all machine learning models (including LSTM) provide desired performance, it shows that the proposed approach of simulating acoustic emission signals can be effective.

Keywords: acoustic emission; bridge cable; deep learning; health monitoring; synthetic data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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