Prediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model

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dc.contributor.authorKim, Mintae
dc.contributor.authorOrdu, Seyma
dc.contributor.authorArslan, Ozkan
dc.contributor.authorKo, Junyoung
dc.date.accessioned2024-10-29T17:58:51Z
dc.date.available2024-10-29T17:58:51Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThis study presents the prediction of the California bearing ratio (CBR) of coarse-and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse-and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R2), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse-and fine-grained soils. The GMDH model had an R2 of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R2 of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse-and fine-grained soils.
dc.description.sponsorshipChungnam National University; National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2022R1C1C1011477]
dc.description.sponsorshipThis work was supported by research fund of Chungnam National University and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1C1C1011477) .
dc.identifier.doi10.12989/gae.2023.33.2.183
dc.identifier.endpage194
dc.identifier.issn2005-307X
dc.identifier.issn2092-6219
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85160359023
dc.identifier.scopusqualityQ2
dc.identifier.startpage183
dc.identifier.urihttps://doi.org/10.12989/gae.2023.33.2.183
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14531
dc.identifier.volume33
dc.identifier.wosWOS:000986574400008
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTechno-Press
dc.relation.ispartofGeomechanics and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial intelligence technology
dc.subjectCalifornia bearing ratio (CBR)
dc.subjectgroup method of data handling (GMDH)
dc.titlePrediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model
dc.typeArticle

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