Exploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data

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dc.authoridKatanalp, Burak Yigit/0000-0002-7172-8192
dc.authoridSert, Sedat/0000-0002-4114-6132
dc.contributor.authorNdepete, Cyrille Prosper
dc.contributor.authorSert, Sedat
dc.contributor.authorBeycioglu, Ahmet
dc.contributor.authorKatanalp, Burak Yigit
dc.contributor.authorEren, Ezgi
dc.contributor.authorBagriacik, Baki
dc.contributor.authorTopolinski, Syzmon
dc.date.accessioned2025-01-06T17:36:28Z
dc.date.available2025-01-06T17:36:28Z
dc.date.issued2022
dc.description.abstractBecause the experimental trials in civil engineering field are difficult and time-consuming, the application of artificial intelligence (AI) techniques is attracting considerable attention, with their use enabling successful results to be more easily obtained. In this study, we investigated the effect of fiber size, fiber amount, water content, and cell pressure on maximum deviator stress (MDS) and failure deformation (FD) of basalt fiber (BF) -reinforced, unsaturated silty soils using three AI techniques: the artificial neural network (ANN), support vector machine (SVM), and fuzzy logic (FL). The numerical analyses and experiments were conducted using varying amounts (1, 1.5, and 2%) and lengths (6, 12, and 24 mm) of BF, and a total of 180 samples were prepared for the detailed investigation. In order to compare model performances, R-2 and MAPE goodness-of-fit metrics were used. The experimental results revealed that the addition of BF generally increased the MDS of the soils, which corresponds to the shearing resistance. According to AI models result, FL outperformed the SVM and ANN, with a R-2 value of 0.938, especially in FD prediction. The sensitivity analysis was performed to ascertain the effect of the inputs on the MDS and FD response variables. Results revealed that fiber length and cell pressure have substantial influence in MDS estimations.
dc.description.sponsorshipPolish National Agency for Academic Exchange [PPI/APM/2019/1/00003]
dc.description.sponsorshipDeclarations The scientific collaborations of this article have been improved by the support of the Polish National Agency for Academic Exchange under Grant No. PPI/APM/2019/1/00003. Besides, the authors would like to express their gratitude to Sakarya University geotechnical engineering laboratory for their contributions to the experimental studies. Please find our paper titled ?Investigation of the effect of basalt fiber size on maximum deviator stress (MDS) and failure defor-mation (FD) of silty soils: A predictive study using AI techniques?. We are submitting for possible publication in Advances in Engineering Software.
dc.identifier.doi10.1016/j.advengsoft.2022.103211
dc.identifier.issn0965-9978
dc.identifier.issn1873-5339
dc.identifier.scopus2-s2.0-85135127549
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.advengsoft.2022.103211
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1892
dc.identifier.volume172
dc.identifier.wosWOS:000843363000002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofAdvances in Engineering Software
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectArtificial neural network
dc.subjectSupport vector machine
dc.subjectFuzzy logic
dc.subjectGeotechnical investigation
dc.titleExploring the effect of basalt fibers on maximum deviator stress and failure deformation of silty soils using ANN, SVM and FL supported by experimental data
dc.typeArticle

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