Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface
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Sustainable Intelligent Infrastructure
Abstract
This study employs Response Surface Methodology (RSM) to model and optimize earthquake-induced ground movements in gravelly geohazard-prone environments. RSM efficiently evaluates the interactions of seismic parameters, including soil type, fault distance, and peak ground acceleration (PGA), reducing computational and experimental efforts. A dataset of 234 entries encompassing 11 seismic and soil stress variables was curated and analyzed, yielding a high-precision predictive model with an R² of 0.9997. The resulting closed-form equation facilitates accurate risk assessment, structural safety optimization, and seismic resilience planning. By identifying critical thresholds and nonlinear relationships, RSM supports cost-effective mitigation strategies, infrastructure design, and retrofitting in earthquake-prone regions.