Mechanically Informed Feature-Enhanced Surrogate Modeling for Seismic Response Prediction and Fragility Assessment of Multi-Ribbed Composite Slab Structures Under Near-Fault Pulse-like Ground Motions

Near-fault pulse-like ground motions produce coupled intensity, duration, and period-matching effects, making nonlinear seismic assessment of multi-ribbed composite slab structures (MCSS) computationally expensive and difficult to generalize. To address this problem, a 4000-case OpenSees nonlinear time-history analysis (NLTHA) database is generated from Wenchuan ground motions through Latin hypercube sampling, and a mechanically informed feature-enhanced deep neural network (MIFE-DNN, previously denoted as PE-DNN in the first submission) is trained using equivalent stiffness, equivalent yield strength, mass proxy, demand-capacity ratios, period-matching ratio, normalized duration, and energy-capacity proxy; a validation-weighted stacked surrogate is further constructed from multi-seed MIFE-DNN and residual learners. On the independent test set, the mean R2 increases from 0.9645 for the ordinary deep neural network (DNN) and 0.9686 for the single MIFE-DNN to 0.9782 for the stacked mechanically informed surrogate, while the maximum inter-story drift-ratio R2 reaches 0.9541. Additional checks include 16 active-learning OpenSees enrichment cases, 12 analyses under two external near-fault records, 3 out-of-domain parameter cases, 100 cross-story tests, SHAP-based interpretation, and multi-EDP fragility post-processing. These checks show that the surrogate is reliable for interpolation and screening within the calibrated equivalent-model domain, but direct OpenSees recalculation is required for boundary, out-of-domain, and cross-configuration use. Parameter-importance, SHAP, and fragility analyses identify peak ground acceleration (PGA), pulse index, period matching, rib height, rib spacing, and damping ratio as dominant factors, indicating that mechanically informed feature-enhanced surrogate modeling provides an interpretable and efficient tool for MCSS response prediction and conditional fragility assessment within the sampled structural and ground-motion domain.