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Structural Monitoring and Maintenance
  Volume 6, Number 2, June 2019 , pages 125-145

Vibration based bridge scour evaluation: A data-driven method using support vector machines
Zhiming Zhang, Chao Sun, Changbin Li and Mingxuan Sun

    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.
Key Words
    bridge scour; modal properties; machine learning; feature extraction; feature selection; support vector machines
Zhiming Zhang and Chao Sun: Department of Civil and Environmental Engineering, Louisiana State University,
3255 Patrick F. Taylor Hall, Baton Rouge, LA 70803, United States
Changbin Li: Department of Computer Science, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, United States
Mingxuan Sun: Division of Computer Science and Engineering, Louisiana State University,
3325 Patrick F. Taylor Hall, Baton Rouge, LA 70803, United States


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