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<p>This study presents a machine learning–based framework for predicting seismic damage states in partially prestressed reinforced concrete bridge piers. A database of 2163 numerically simulated bridge columns was developed using Latin Hypercube Sampling, covering wide variations in geometry, material strengths, axial load ratios, and prestressing levels representing a diverse range of demand–capacity conditions. Ten supervised learning algorithms including support vector machines, neural networks, random forests, gradient boosting methods, and CatBoost were trained and evaluated through multiple statistical metrics. Model predictions were validated against experimental results for selected column specimens, demonstrating strong agreement between simulations and physical behavior. Among the evaluated models, the Deep Neural Network (DNN) exhibited the highest overall predictive accuracy, achieving Pearson correlation coefficients of 0.982, 0.998, 0.9996, 0.988, and 0.9989 for residual displacement, residual force, force at collapse drift, hysteretic energy, and maximum base-shear, respectively. The corresponding <i>RMSE</i> values were 0.0275, 0.0023, 0.0036, 0.0281, and 0.0293. Although, CatBoost and Artificial Neural Network showed slightly better performance than the DNN in predicting hysteretic energy, the DNN remained the most reliable model across the structural responses examined. Overall, it provides a robust and consistent tool for damage estimation and performance assessment of prestressed Reinforced Concrete bridge piers subjected to seismic loading.</p>

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Rapid seismic damage prediction of prestressed concrete bridge columns using validated machine learning models

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • A. Abdolmaleki,
  • S. Mahboubi

摘要

This study presents a machine learning–based framework for predicting seismic damage states in partially prestressed reinforced concrete bridge piers. A database of 2163 numerically simulated bridge columns was developed using Latin Hypercube Sampling, covering wide variations in geometry, material strengths, axial load ratios, and prestressing levels representing a diverse range of demand–capacity conditions. Ten supervised learning algorithms including support vector machines, neural networks, random forests, gradient boosting methods, and CatBoost were trained and evaluated through multiple statistical metrics. Model predictions were validated against experimental results for selected column specimens, demonstrating strong agreement between simulations and physical behavior. Among the evaluated models, the Deep Neural Network (DNN) exhibited the highest overall predictive accuracy, achieving Pearson correlation coefficients of 0.982, 0.998, 0.9996, 0.988, and 0.9989 for residual displacement, residual force, force at collapse drift, hysteretic energy, and maximum base-shear, respectively. The corresponding RMSE values were 0.0275, 0.0023, 0.0036, 0.0281, and 0.0293. Although, CatBoost and Artificial Neural Network showed slightly better performance than the DNN in predicting hysteretic energy, the DNN remained the most reliable model across the structural responses examined. Overall, it provides a robust and consistent tool for damage estimation and performance assessment of prestressed Reinforced Concrete bridge piers subjected to seismic loading.