Steel and Composite Structures

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Damage identification in suspension bridges under earthquake excitation using practical advanced analysis and hybrid machine-learning models

  • Van-Thanh Pham (Department of Civil and Environmental Engineering, Sejong University) ;
  • Duc-Kien Thai (Faculty of Civil Engineering, Thuyloi University) ;
  • Seung-Eock Kim (Faculty of Civil Engineering, Thuyloi University)
  • Received : 2024.08.08
  • Accepted : 2024.09.23
  • Published : 2024.09.25
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Abstract

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

Keywords

Acknowledgement

The research described in this paper was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A2B5B01002577).

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