Research on the Application of Machine Learning in Bridge Structural Health Monitoring

Xinshuang Liu

doi:10.62051/njg0yj61

Authors

Xinshuang Liu

DOI:

https://doi.org/10.62051/njg0yj61

Keywords:

Machine Learning; Structural Health Monitoring System; Data Processing; Bridge Engineering.

Abstract

Bridge structural health monitoring (BSHM) is an effective measure to monitor bridge operation status, identify bridge damage and give early warning. In the era of rapid development of the Internet, the combination of computer algorithm and BSHM can greatly improve the efficiency and accuracy of monitoring. This paper discusses the application key of machine learning (ML) in BSHM. First, the basic concepts of BSHM and ML algorithms are outlined respectively. Then, the advantages of BSHM compared with traditional detection methods are analyzed. In addition, the composition, sensor technology, data transmission and data processing of the BSHM system are discussed in detail. Among them, data processing includes preprocessing, fusion, identification and visualization. In these processes, ML is particularly important, playing a crucial role in pre-processing and data analysis. Finally, the advantages and disadvantages of the application of ML algorithm in health detection are discussed, and its future development direction is prospected.

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