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Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning

Zhang, R.; Wu, C.; Goh, A. C.; Böhlke, T.; Zhang, W.

Abstract (englisch):
This paper adopts the NGI-ADP soil model to carry out finite element analysis, based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated. More than one thousand finite element cases were numerically analyzed, followed by extensive parametric studies. Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression (DTR), Multilayer Perceptron Regression (MLPR), and Multivariate Adaptive Regression Splines (MARS). This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast, alternative way.

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Verlagsausgabe §
DOI: 10.5445/IR/1000118856
Veröffentlicht am 20.02.2021
Originalveröffentlichung
DOI: 10.1016/j.gsf.2020.03.003
Scopus
Zitationen: 8
Web of Science
Zitationen: 4
Dimensions
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 01.2021
Sprache Englisch
Identifikator ISSN: 1674-9871, 2588-9192
KITopen-ID: 1000118856
Erschienen in Geoscience Frontiers
Verlag Elsevier
Band 12
Heft 1
Seiten 365-373
Vorab online veröffentlicht am 19.03.2020
Schlagwörter Anisotropic clay, NGI-ADP, Wall deflection, Ensemble learning, eXtreme gradient boosting, Random forest regression
Nachgewiesen in Dimensions
Web of Science
Scopus
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