Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties

Basit Öğe Kaydı
dc.authorscopusid56754342900
dc.authorscopusid57209331250
dc.authorscopusid57907081600
dc.authorscopusid26325060700
dc.authorscopusid57203165669
dc.authorscopusid55764279800
dc.contributor.authorKim, Mintae
dc.contributor.authorOkuyucu, Osman
dc.contributor.authorOrdu, Ertuğrul
dc.contributor.authorOrdu, Şeyma
dc.contributor.authorArslan, Özkan
dc.contributor.authorKo, Junyoung
dc.date.accessioned2023-04-20T08:01:15Z
dc.date.available2023-04-20T08:01:15Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü
dc.description.abstractThis study presents a novel method for predicting the undrained shear strength (c(u)) using artificial intelligence technology. The c(u) value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of c(u). The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (sigma(v)'), standard penetration test result (N-SPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R-2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., sigma(v)', N-SPT, LL, PL, PI), had a higher correlation coefficient (R-2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of c(u) value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.
dc.description.sponsorshipChungnam National University; National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF2022R1C1C1011477]
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. NRF2022R1C1C1011477).
dc.identifier.doi10.3390/ma15186385
dc.identifier.issn1996-1944
dc.identifier.issue18en_US
dc.identifier.pmid36143696
dc.identifier.scopus2-s2.0-85138797306
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/ma15186385
dc.identifier.urihttps://hdl.handle.net/20.500.11776/10826
dc.identifier.volume15
dc.identifier.wosWOS:000856875300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorOkuyucu, Osman
dc.institutionauthorOrdu, Ertuğrul
dc.institutionauthorOrdu, Şeyma
dc.institutionauthorArslan, Özkan
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofMaterials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectUndrained Shear Strength
dc.subjectStandard Penetration Test
dc.subjectGroup Method Of Data Handling
dc.subjectRandom Forest
dc.subjectSupport Vector Regression
dc.subjectExtreme Gradient Boosting
dc.subjectFine-Grained Soils
dc.subjectRandom Forest
dc.subjectRegression
dc.subjectModels
dc.subjectTrees
dc.titlePrediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
10826.pdf
Boyut:
3.74 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Çorlu Mühendislik Fakültesi Koleksiyonu