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<p>The seismic fragility of reinforced concrete highway bridges has been found profoundly affected by corrosion-induced degradation. Traditional fragility analysis methods such as the Cloud and Incremental Dynamic Analysis, while effective, they are computationally intensive and impractical for large-scale regional risk and resilience assessments. Additionally, they heavily rely on a predefined probabilistic assumption (i.e., the lognormal distribution in seismic demand and capacity), whose validity remains unknown for corroded structures. To bridge this gap and circumvent this assumption, this study leverages machine learning (ML) to develop a damage-state-classification-driven seismic fragility modeling approach for corroded reinforced concrete bridge portfolios. A comprehensive database is developed through nonlinear time-history analyses, incorporating the effects of bridge structural variability, corrosion levels, and diverse seismic scenarios. Three popular ML classifiers, including artificial neural network (ANN), support vector machine, and <i>K</i>-nearest neighbor, are trained and rigorously optimized to map probabilities of damage states directly from structural features and seismic inputs. Analysis results showcase the efficiency and scalability of the ML-empowered method, which significantly reduces computational effort while maintaining well alignment with results from the Cloud method, particularly for the ANN-based one.</p>

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Machine learning-empowered highly efficient seismic fragility estimation of corroded reinforced concrete highway bridge portfolios

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Bo Xu,
  • Xiaowei Wang,
  • Yue Li

摘要

The seismic fragility of reinforced concrete highway bridges has been found profoundly affected by corrosion-induced degradation. Traditional fragility analysis methods such as the Cloud and Incremental Dynamic Analysis, while effective, they are computationally intensive and impractical for large-scale regional risk and resilience assessments. Additionally, they heavily rely on a predefined probabilistic assumption (i.e., the lognormal distribution in seismic demand and capacity), whose validity remains unknown for corroded structures. To bridge this gap and circumvent this assumption, this study leverages machine learning (ML) to develop a damage-state-classification-driven seismic fragility modeling approach for corroded reinforced concrete bridge portfolios. A comprehensive database is developed through nonlinear time-history analyses, incorporating the effects of bridge structural variability, corrosion levels, and diverse seismic scenarios. Three popular ML classifiers, including artificial neural network (ANN), support vector machine, and K-nearest neighbor, are trained and rigorously optimized to map probabilities of damage states directly from structural features and seismic inputs. Analysis results showcase the efficiency and scalability of the ML-empowered method, which significantly reduces computational effort while maintaining well alignment with results from the Cloud method, particularly for the ANN-based one.