Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年3月1日

查看全文
  • Xueqi Zhong,
  • Lintao Tang,
  • Xiangnan Li,
  • Liuyang Li

摘要

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.