Toward efficient multi-objective seismic design optimization of self-centering bridges using machine learning

Abstract

Self-centering rocking bridge piers, characterized by their minimal residual deformation and rapid post-seismic recovery, have emerged as a promising solution for enhancing the seismic resilience of bridge systems. However, their inherently nonlinear behavior and pronounced sensitivity to multiple interdependent design parameters make it challenging to achieve balanced seismic performance among all piers within an integrated bridge system. This work develops a system-oriented optimization framework for self-centering rocking bridges to address this issue. The proposed framework integrates machine learning-based surrogate modeling to markedly accelerate the optimization process. A detailed case study of a four-span self-centering rocking bridge is conducted to demonstrate the framework’s applicability and effectiveness. Results show that substituting traditional finite element model with an XGBoost-based surrogate model reduces computational time by 92% while preserving high predictive accuracy. Furthermore, the optimized design significantly enhances system-level performance uniformity, achieving a 52.3% reduction in inter-pier shear force variability and a 19.0% decrease in displacement disparity compared with the baseline configuration.

Graphical abstract

Keywords

self-centering rocking bridge / machine learning / seismic design / multi-objective optimization / nonlinear dynamics / multi-criteria decision-making

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