A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

dc.contributor.authorYaghoubi, E.
dc.contributor.authorYaghoubi, E.
dc.contributor.authorKhamees, A.
dc.contributor.authorVakili, A.H.
dc.date.accessioned2024-09-29T16:21:13Z
dc.date.available2024-09-29T16:21:13Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractArtificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem. © The Author(s) 2024.en_US
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKen_US
dc.identifier.doi10.1007/s00521-024-09893-7
dc.identifier.endpage12699en_US
dc.identifier.issn0941-0643
dc.identifier.issue21en_US
dc.identifier.scopus2-s2.0-85192811107en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage12655en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-024-09893-7
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9628
dc.identifier.volume36en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectDeep learningen_US
dc.subjectEnsemble learningen_US
dc.subjectGeotechnical engineeringen_US
dc.subjectMachine learningen_US
dc.titleA systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineeringen_US
dc.typeReviewen_US

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