Deep Learning Approach on Prediction of Soil Consolidation Characteristics

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dc.authoridSenturk, Muharrem Atakan/0000-0002-1230-3442
dc.authoridKim, Mintae/0000-0003-3346-1746
dc.contributor.authorKim, Mintae
dc.contributor.authorSenturk, Muharrem A.
dc.contributor.authorTan, Rabia K.
dc.contributor.authorOrdu, Ertugrul
dc.contributor.authorKo, Junyoung
dc.date.accessioned2024-10-29T17:59:19Z
dc.date.available2024-10-29T17:59:19Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractArtificial neural network models, crucial for accurate predictions, should be meticulously designed for specific problems using deep learning-based algorithms. In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. The consolidation features of fine-grained soil have a significant impact on the stability of structures, particularly in terms of long-term stability. Precise prediction of soil consolidation under planned structures is vital for effective foundation design. The compression index (Cc) is an important parameter used in predicting consolidation settlement in soils. Therefore, this study examines the use of deep learning techniques, which are types of artificial neural network algorithms with deep layers, in predicting compression index (Cc) in geotechnical engineering. Four neural network models with different architectures and hyperparameters were modeled and evaluated using performance metrics such as mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset contains 916 samples with variables such as natural water content (w), liquid limit (LL), plasticity index (PI), and compression index (Cc). This approach allows the results of soil consolidation tests to be seen more quickly at less cost, although predictively. The findings demonstrate that deep learning models are an effective tool in predicting consolidation of fine-grained soil and offering significant opportunities for applications in geotechnical engineering. This study contributes to a more accurate prediction of soil consolidation, which is critical for the long-term stability of structural designs.
dc.description.sponsorshipNational Research Foundation of Korea (NRF)
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.3390/buildings14020450
dc.identifier.issn2075-5309
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85185847543
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/buildings14020450
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14702
dc.identifier.volume14
dc.identifier.wosWOS:001172217200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofBuildings
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectcompression index
dc.subjectdeep learning
dc.subjectmultilayer perceptron
dc.subjectconvolutional neural network
dc.titleDeep Learning Approach on Prediction of Soil Consolidation Characteristics
dc.typeArticle

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