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<p>This study presents a comprehensive investigation of the performance assessment of reinforced concrete (RC) multistory buildings with varying shear wall configurations under seismic and wind loading conditions, utilizing ETABS software for structural analysis. Six building models were designed, each with distinct arrangements of shear walls, including their placement in the X and Y directions. These configurations were evaluated on the basis of key structural performance parameters such as lateral displacement, interstory drift, and shear force, following the guidelines of IS:875-2002. Among the six configurations, Model 4, which incorporates shear walls in both the X and Y directions, demonstrated the most effective performance by minimizing displacements and enhancing the overall lateral resistance. To further support efficient structural design and reduce computational demand, this study integrated machine learning (ML) techniques for predictive analysis. A dataset comprising 153 samples was generated from the ETABS analytical results, capturing critical parameters such as model type, story height, displacement, drift, and shear. Three supervised regression models, a decision tree regressor, a random forest regressor, and a multilayer perceptron (MLP), were trained and tested to predict structural responses. Among these, the random forest regressor achieved the best balance between accuracy and generalization, demonstrating its robustness in modeling complex structural behavior. This dual-phase methodology, which combines detailed finite element analysis with data-driven ML modeling, provides a novel and practical framework for evaluating the seismic and wind performance of multistory RC buildings. The results offer valuable insights for structural engineers, enabling quicker decision-making and optimized design strategies in performance-based design and urban construction planning.</p>

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Seismic and wind load assessment of multistory RC structures with integrated machine learning-based prediction models

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • R. Shanthi Vengadeshwari,
  • M. S. Ujwal,
  • N. C. Sanjay Shekar,
  • T. N. Akash,
  • K. Sahana,
  • G. Shiva Kumar

摘要

This study presents a comprehensive investigation of the performance assessment of reinforced concrete (RC) multistory buildings with varying shear wall configurations under seismic and wind loading conditions, utilizing ETABS software for structural analysis. Six building models were designed, each with distinct arrangements of shear walls, including their placement in the X and Y directions. These configurations were evaluated on the basis of key structural performance parameters such as lateral displacement, interstory drift, and shear force, following the guidelines of IS:875-2002. Among the six configurations, Model 4, which incorporates shear walls in both the X and Y directions, demonstrated the most effective performance by minimizing displacements and enhancing the overall lateral resistance. To further support efficient structural design and reduce computational demand, this study integrated machine learning (ML) techniques for predictive analysis. A dataset comprising 153 samples was generated from the ETABS analytical results, capturing critical parameters such as model type, story height, displacement, drift, and shear. Three supervised regression models, a decision tree regressor, a random forest regressor, and a multilayer perceptron (MLP), were trained and tested to predict structural responses. Among these, the random forest regressor achieved the best balance between accuracy and generalization, demonstrating its robustness in modeling complex structural behavior. This dual-phase methodology, which combines detailed finite element analysis with data-driven ML modeling, provides a novel and practical framework for evaluating the seismic and wind performance of multistory RC buildings. The results offer valuable insights for structural engineers, enabling quicker decision-making and optimized design strategies in performance-based design and urban construction planning.