Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Steel and Composite Structures Volume 52, Number 6, September 25 2024 , pages 695-711 DOI: https://doi.org/10.12989/scs.2024.52.6.695 |
|
|
|
Damage identification in suspension bridges under earthquake excitation using practical advanced analysis and hybrid machine-learning models |
||
Van-Thanh Pham, Duc-Kien Thai and Seung-Eock Kim
|
||
| 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. | ||
| Key Words | ||
| categorical gradient boosting; damage identification; earthquake excitation; machine learning; practical advanced analysis; suspension bridge | ||
| Address | ||
| Van-Thanh Pham:1)Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, South Korea 2)Faculty of Civil Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam Duc-Kien Thai:Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, South Korea Seung-Eock Kim:Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, South Korea | ||