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<p>Seismic performance of reinforced concrete (RC) framed buildings is strongly influenced by architectural and structural features such as façade elements, soft storeys, and shear walls. These components interact in complex ways to modify stiffness distribution, force transfer mechanisms, and deformation demand, particularly in high seismic zones. This study presents a comprehensive comparative evaluation of the seismic behavior of RC framed buildings incorporating façade elements, vertical stiffness irregularities due to soft storeys, and shear wall systems. Eight three-dimensional 15-storey RC building models were developed and analysed using ETABS under gravity, wind, and seismic loading in accordance with IS 1893 provisions. Linear dynamic response spectrum analysis and nonlinear time-history analysis were employed to assess key performance indicators, including storey displacement, inter-storey drift, story shear, base reactions, and dynamic characteristics. In addition, a machine learning (ML) framework was developed using a dataset generated from numerical simulations to predict structural response parameters using Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) regressors. The results indicate that shear wall–equipped models exhibit significant reductions in roof displacement and inter-storey drift by approximately 60–70%, along with an increase in base shear capacity of 120–140% compared to frame-only configurations. Soft storeys were found to amplify deformation demand in the absence of shear walls, while their influence was effectively suppressed in shear wall–dominated systems. Base reaction heat map analysis revealed pronounced load concentration at shear wall locations, highlighting important foundation design implications. Among the ML models, the Decision Tree and Random Forest regressors demonstrated superior predictive accuracy (R² up to 0.95), whereas the MLP showed comparatively lower robustness. The study provides integrated performance-based insights for seismic design, retrofit decision-making, and data-driven structural response prediction of RC buildings in earthquake-prone regions.</p>

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Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • M. S. Ujwal,
  • N. C. Sanjay Shekar,
  • Arya Prathap,
  • C. B. Ranjan Gowda,
  • N. Vibhashree,
  • M. Karthik,
  • G. Shiva Kumar

摘要

Seismic performance of reinforced concrete (RC) framed buildings is strongly influenced by architectural and structural features such as façade elements, soft storeys, and shear walls. These components interact in complex ways to modify stiffness distribution, force transfer mechanisms, and deformation demand, particularly in high seismic zones. This study presents a comprehensive comparative evaluation of the seismic behavior of RC framed buildings incorporating façade elements, vertical stiffness irregularities due to soft storeys, and shear wall systems. Eight three-dimensional 15-storey RC building models were developed and analysed using ETABS under gravity, wind, and seismic loading in accordance with IS 1893 provisions. Linear dynamic response spectrum analysis and nonlinear time-history analysis were employed to assess key performance indicators, including storey displacement, inter-storey drift, story shear, base reactions, and dynamic characteristics. In addition, a machine learning (ML) framework was developed using a dataset generated from numerical simulations to predict structural response parameters using Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) regressors. The results indicate that shear wall–equipped models exhibit significant reductions in roof displacement and inter-storey drift by approximately 60–70%, along with an increase in base shear capacity of 120–140% compared to frame-only configurations. Soft storeys were found to amplify deformation demand in the absence of shear walls, while their influence was effectively suppressed in shear wall–dominated systems. Base reaction heat map analysis revealed pronounced load concentration at shear wall locations, highlighting important foundation design implications. Among the ML models, the Decision Tree and Random Forest regressors demonstrated superior predictive accuracy (R² up to 0.95), whereas the MLP showed comparatively lower robustness. The study provides integrated performance-based insights for seismic design, retrofit decision-making, and data-driven structural response prediction of RC buildings in earthquake-prone regions.