Physics-Guided Machine Learning for Rapid Nonlinear Seismic Safety Assessment of Reinforced-Concrete Frame–Wall Buildings
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I3P104Keywords:
Reinforced-Concrete Buildings, Nonlinear Seismic Analysis, Machine Learning, Rapid Seismic Safety Assessment, ETABS, Inter-Storey Drift, Performance-Based Earthquake Engineering, Structural Health MonitoringAbstract
Purpose: This study develops a machine-learning-assisted framework for rapid seismic safety assessment of reinforced-concrete frame-wall buildings using nonlinear seismic analysis outputs. The framework is intended to reduce the time required for preliminary post-earthquake structural evaluation while retaining key indicators of nonlinear building response, including inter-storey drift, roof displacement, plastic-hinge development, and seismic performance state. Methodology: A detailed reinforced-concrete building model is developed in ETABS using available structural geometry, material properties, member sections, loading conditions, diaphragm assignments, and seismic parameters. Nonlinear static pushover and nonlinear response-history analyses are used to generate seismic-response data under systematically varied structural and ground-motion conditions. Structural properties, elastic-response indicators, modal characteristics, and earthquake-intensity measures are used as input variables for machine-learning models. Regression models predict continuous seismic-demand parameters, while classification models identify building safety states. Model performance is evaluated using grouped validation procedures, prediction-error measures, safety-classification metrics, and explainable feature-importance analysis. Findings: The study is expected to show that machine-learning models trained on nonlinear seismic simulations can estimate key response measures and classify seismic safety conditions with substantially lower computational time than repeated nonlinear analyses. The evaluation will identify the most reliable model for drift and safety-state prediction, quantify false-safe classification risk, and determine the structural and seismic variables that most strongly influence predicted performance. Unique Contribution to Theory, Practice and Policy: The study contributes a physics-guided, data-driven seismic assessment approach that links detailed nonlinear structural analysis with rapid predictive modelling for reinforced-concrete buildings. It provides a practical basis for prioritising detailed inspections, emergency-response decisions, and post-earthquake safety screening. The framework should be applied as a decision-support tool alongside professional engineering judgement, particularly where predicted risk or model uncertainty is high.
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