Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques

Basit Öğe Kaydı
dc.authorid0000-0002-0180-7362en_US
dc.authorid0000-0002-2837-3306en_US
dc.authorid0000-0002-6122-9066en_US
dc.authorid0000-0003-3206-8133en_US
dc.authorid0000-0003-1973-4437en_US
dc.contributor.authorKaya, Zülküf
dc.contributor.authorLatifoğlu, Levent
dc.contributor.authorUncuoğlu, Erdal
dc.contributor.authorErol, Aykut
dc.contributor.authorKeskin, Mehmet Salih
dc.date.accessioned2023-09-29T06:02:19Z
dc.date.available2023-09-29T06:02:19Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractLandslides refer to a wide range of processes that result in the downward and outward movement of slope-forming materials, which may spread. Estimating lateral spreading of soil is essential because of the complexities associated with the lateral spreading behavior. Existing empirical models for predicting liquefaction-induced lateral spread displacement are developed using a dataset that varied in terms of earthquake magnitude, source distance, ground slope, layer thickness, fines content, and grain size. The aim of this study is to increase the accuracy of earthquake-induced lateral spreading prediction using multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) model. MGGP, MLP, and RF model predictions of lateral spreading are compared with the results anticipated using machine learning techniques and conventional approaches. Results showed that the MGGP outperforms the Hamada, Youd, MLP, and RF equations for estimating maximum lateral displacement under free-face and gently sloping ground conditions according to the comparisons. The MGGP, which is proved to be better, was also utilized to estimate total lateral displacement for Adapazari data, along with machine learning techniques and conventional approaches.en_US
dc.identifier.citationKaya, Z., Latifoğlu, L., Uncuoğlu, E., Erol, A. ve Keskin, M. S. (2023). Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques. Bulletin of Engineering Geology and the Environment, 82(3), 1-18.en_US
dc.identifier.doi10.1007/s10064-023-03103-9
dc.identifier.endpage18en_US
dc.identifier.issn1435-9529
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85148691458
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s10064-023-03103-9
dc.identifier.urihttps://hdl.handle.net/11468/12630
dc.identifier.volume82en_US
dc.identifier.wosWOS:000936146600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKeskin, Mehmet Salih
dc.language.isoenen_US
dc.publisherSpringer Science and Business Mediaen_US
dc.relation.ispartofBulletin of Engineering Geology and the Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLateral spreadingen_US
dc.subjectMGGPen_US
dc.subjectMLPen_US
dc.subjectRFen_US
dc.subjectKocaeli earthquakeen_US
dc.titlePredicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniquesen_US
dc.titlePredicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Predicting liquefaction-induced lateral spreading by using the multigene genetic.pdf
Boyut:
1.98 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

İnşaat Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu