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 rela... More >

The inaugural editorial of Sustainable Intelligent Infrastructure introduces the journal's mission to address global challenges such as urbanization, climate change, and resource depletion by integrating sustainability principles with advanced technologies. It aims to provide a multidisciplinary platform for high-quality research that explores the synergy between smart technologies, artificial intelligence, machine learning, and data analytics in sustainable infrastructure. The journal aspires to become a leading voice in global discourse, bridging the gap between traditional infrastructure and intelligent systems while driving innovation to meet the United Nations' Sustainable Development G... More >