Structural Monitoring and Maintenance

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Vibration based bridge scour evaluation: A data-driven method using support vector machines

  • Zhang, Zhiming (Department of Civil and Environmental Engineering, Louisiana State University) ;
  • Sun, Chao (Department of Civil and Environmental Engineering, Louisiana State University) ;
  • Li, Changbin (Department of Computer Science, University of Texas at Dallas) ;
  • Sun, Mingxuan (Division of Computer Science and Engineering, Louisiana State University)
  • Received : 2019.02.28
  • Accepted : 2019.04.18
  • Published : 2019.06.25
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Abstract

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

Keywords

Acknowledgement

Supported by : Louisiana State University

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