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<p>This study examines the seismic response of plan-irregular reinforced concrete (RC) buildings with C-, I-, L-, and T-shaped configurations, integrating statistical and machine learning (ML) techniques to quantify the influence of irregularity on structural performance. Focusing on five plan irregularity ratios (L/B, L/B<sub>1</sub>, B/L<sub>2</sub>, B<sub>2</sub>/B, L<sub>1</sub>/L), The paper evaluated their impact on key seismic responses—maximum storey drift, roof displacement, roof acceleration, and base shear—under far-fault (FF) and near-fault (NF-PL, NF-NP) ground motions across 16 building models. Pearson correlation analysis revealed moderate to strong linear relationships, with L/B significantly influencing base shear (<i>r</i> = 0.71) and roof displacement, though it lacked depth in capturing shape-specific effects. One-Way ANOVA showed that building shape alone was not consistently significant (<i>p</i> &gt; 0.05, e.g., Max Drift-Y: <i>p</i> = 0.27), but ground motion effects were shape-specific (e.g., T-shape Roof Displacement in Y: <i>p</i> ≈ 0.041). Two-way ANOVA confirmed significant shape-motion interactions, notably for roof displacement (<i>p</i> = 0.012). XGBoost with SHAP analysis identified L/B as the dominant feature (SHAP of 1033.1 for base shear in I-shaped, Y-direction), with B<sub>2</sub>/B affecting drift in T-shaped buildings. PCA and K-Means clustering revealed four latent groups, with Cluster 2 exhibiting extreme responses (base shear of 19,789 kN) driven by a high L/B<sub>1</sub> ratio (5.5). These findings advocate for parameter-driven design over shape-based classifications, offering a data-driven foundation for fragility modelling and code refinement in seismic engineering.</p>

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Seismic sensitivity analysis of plan-irregular RC buildings using statistical and machine learning approaches

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Amit Thoriya,
  • Husain Rangwala,
  • Dipesh Kamati,
  • Tarak Vora

摘要

This study examines the seismic response of plan-irregular reinforced concrete (RC) buildings with C-, I-, L-, and T-shaped configurations, integrating statistical and machine learning (ML) techniques to quantify the influence of irregularity on structural performance. Focusing on five plan irregularity ratios (L/B, L/B1, B/L2, B2/B, L1/L), The paper evaluated their impact on key seismic responses—maximum storey drift, roof displacement, roof acceleration, and base shear—under far-fault (FF) and near-fault (NF-PL, NF-NP) ground motions across 16 building models. Pearson correlation analysis revealed moderate to strong linear relationships, with L/B significantly influencing base shear (r = 0.71) and roof displacement, though it lacked depth in capturing shape-specific effects. One-Way ANOVA showed that building shape alone was not consistently significant (p > 0.05, e.g., Max Drift-Y: p = 0.27), but ground motion effects were shape-specific (e.g., T-shape Roof Displacement in Y: p ≈ 0.041). Two-way ANOVA confirmed significant shape-motion interactions, notably for roof displacement (p = 0.012). XGBoost with SHAP analysis identified L/B as the dominant feature (SHAP of 1033.1 for base shear in I-shaped, Y-direction), with B2/B affecting drift in T-shaped buildings. PCA and K-Means clustering revealed four latent groups, with Cluster 2 exhibiting extreme responses (base shear of 19,789 kN) driven by a high L/B1 ratio (5.5). These findings advocate for parameter-driven design over shape-based classifications, offering a data-driven foundation for fragility modelling and code refinement in seismic engineering.