Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface
| dc.contributor.author | Kennedy C. Onyelowe | |
| dc.contributor.author | Denise‐Penelope N. Kontoni | |
| dc.contributor.author | Fortune K. C. Onyelowe | |
| dc.contributor.author | Viroon Kamchoom | |
| dc.contributor.author | Shadi Hanandeh | |
| dc.contributor.author | Ahmed M. Ebid | |
| dc.contributor.author | Néstor Ulloa | |
| dc.contributor.author | Arif Ali Baig Moghal | |
| dc.contributor.author | M. Vishnupriyan | |
| dc.date.accessioned | 2026-05-08T19:20:32Z | |
| dc.date.issued | 2025-3-24 | |
| dc.description.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. | |
| dc.identifier.doi | 10.62762/sii.2025.846883 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17592 | |
| dc.publisher | Sustainable Intelligent Infrastructure | |
| dc.subject | Structural Health Monitoring Techniques | |
| dc.subject | Seismic Performance and Analysis | |
| dc.subject | Landslides and related hazards | |
| dc.title | Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface | |
| dc.type | Article |