Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface

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.

Authors

Institutions

Publication Details

Journal
Sustainable Intelligent Infrastructure
Published
2025-03-24
DOI
https://doi.org/10.62762/sii.2025.846883
Citations
2
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
1.91
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface

Ahmed M. Ebid, Shadi Hanandeh, Arif Ali Baig Moghal, Fortune K. C. Onyelowe et al.
2 citations
Sustainable Intelligent Infrastructure
Structural Health Monitoring Techniques
1.91
article

Forecasting Earthquake-induced Ground Movement under Seismic Activity Using Response Surface

Ahmed M. Ebid, Shadi Hanandeh, Arif Ali Baig Moghal, Fortune K. C. Onyelowe, Viroon Kamchoom‬, Néstor Ulloa, Denise‐Penelope N. Kontoni, Kennedy C. Onyelowe, M. Vishnupriyan
article en
2 citations

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.

Sustainable Intelligent InfrastructureVol. 1(1)
National Institute of Technology Warangal (IN), University of Peloponnese (GR), Future University in Egypt (EG), Hellenic Open University (GR), Kampala International University (UG), Al-Balqa Applied University (JO), SRM University (IN), Escuela Superior Politécnica del Chimborazo (EC), King Mongkut's Institute of Technology Ladkrabang (TH), Michael Okpara University of Agriculture (NG)
Openalex Percentile: Top 17%
Structural Health Monitoring Techniques
1.91
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.