ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil | NOVA. The University of Newcastle's Digital Repository

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/806769

Title: ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil
Author/Creator: Kuo, Y. L.; Jaksa, M. B.; Lyamin, A. V.; Kaggwa, W. S.
Institution: The University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering
Description: In reality, footings are most likely to be founded on multi-layered soils. The existing methods for predicting the bearing capacity of 4-layer up to 10-layer cohesive soil are inaccurate. This paper aims to develop a more accurate bearing capacity prediction method based on multiple regression methods and multi-layer perceptrons (MLPs), one type of artificial neural networks (ANNs). Predictions of bearing capacity from the developed multiple regression models and MLP in tractable equations form are obtained and compared with the value predicted using traditional methods. The results indicate ANNs are able to predict accurately the bearing capacity of strip footing and outperform the existing methods.
Relation: Computers and Geotechnics Vol. 36, Issue 3, p. 503-516
Publisher Link: http://dx.doi.org/10.1016/j.compgeo.2008.07.002
Date: 2009
Publisher: Elsevier
Keyword(s): bearing capacity; cohesive soil; limit analysis; multilayered cohesive material; strip footing; neural network
Resource Type: journal article
Identifier: http://hdl.handle.net/1959.13/806769
Identifier: ISSN:0266-352X

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