Seismic fragility analysis of elevated RC tanks based on IDA and machine learning

Elevated reinforced concrete (RC) water tanks are critical lifeline structures whose seismic performance is governed by complex fluid–structure interaction (FSI) effects and slender staging systems. Conventional fragility assessment via incremental dynamic analysis (IDA) yields probabilistic insights but entails extensive nonlinear time history simulations that limit practical application. This study presents a hybrid framework that couples IDA with machine learning (ML) to expedite the generation of seismic fragility curves for three Indian Standard–compliant RC tank configurations (75 m ³ , 320 m ³ , 1008 m ³ ). Validated finite element (FE) models in SAP2000 incorporate Housner’s added mass formulation to represent hydrodynamic demands. IDA under 22 far-field ground motions produces 738 nonlinear response samples characterized by ground motion characteristics and key geometric parameters. Support vector regression (SVR) and multilayer perceptron (MLP) regressors are trained to predict peak inter-story drift ratio (IDR), with hyperparameters optimized via Bayesian search and interpretability assessed through SHapley Additive exPlanations (SHAP) analysis. MLP achieves superior fidelity (test R ²  = 0.990, RMSE = 0.0009) compared to SVR (R ²  = 0.953, RMSE = 0.0021), maintaining errors below 6% for collapse-level exceedance probabilities. ML-derived fragility curves closely match IDA baselines, capturing threshold transitions and dispersion. The proposed approach enables rapid, code-compliant fragility evaluation—bridging probabilistic rigor and computational efficiency—and supports performance-based seismic design, retrofit prioritization and resilience planning for RC water infrastructure in seismically active regions.