Low-Data Modeling of Shallow Foundations on Cohesive Slopes: A Comparison between Hybrid FEM-RSM and ML Models

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dc.contributor.authorKamiloglu, Hakan Alper
dc.contributor.authorGucuyener, Alper
dc.date.accessioned2026-09-01T15:52:35Z
dc.date.available2026-09-01T15:52:35Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractIn geotechnical engineering, accurately predicting the seismic bearing capacity of shallow foundations on cohesive slopes requires proper consideration of nonlinear effects and parameter interactions. However, most studies in the literature address these complex effects inadequately, either with numerical methods that demand a large number of analyses or with machine learning (ML) models that require large and heterogeneous data sets. In this study, it is aimed to propose an approach that allows for a robust analysis of the system and at the same time provides accurate forecasting for situations where working with small and homogeneous data sets is mandatory. Within the scope of the study, seismic bearing capacity analyses were performed using Plaxis 2D for 273 cases where eight independent variables took different values according to the Face Centered Composite Design (FCCD), and a database was created. Multiple Linear (MLR), Multiple Nonlinear (MNLR) Regression Models were established, and ML models, such as Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were trained. The models were compared with both in-sample and out-of-sample estimation performance, and parametric sensitivity analyses were evaluated by methods such as ANOVA and SHAP. The results show that the MNLR model with natural logarithmic transformation is the most successful method in terms of both accuracy (R2 approximate to 0.98) and reflecting parameter interactions, while the SVR algorithm demonstrates the best generalization ability among ML models under low-data conditions. The results also confirm that the ML models effectively capture the parameter effects.
dc.identifier.doi10.18400/tjce.1711510
dc.identifier.endpage79
dc.identifier.issn2822-6836
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105037928092
dc.identifier.scopusqualityQ3
dc.identifier.startpage47
dc.identifier.urihttp://dx.doi.org/10.18400/tjce.1711510
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8468
dc.identifier.volume37
dc.identifier.wosWOS:001761218700003
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTurkish Chamber Civil Engineers
dc.relation.ispartofTurkish Journal of Civil Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectSeismic Bearing Capacity
dc.subjectResponse Surface Methodology
dc.subjectFinite Element Method
dc.subjectSoft Computing Methods
dc.titleLow-Data Modeling of Shallow Foundations on Cohesive Slopes: A Comparison between Hybrid FEM-RSM and ML Models
dc.typeArticle

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