ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil

Title: ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil
Creator: Kuo, Y. L.; Jaksa, M. B.; Lyamin, A. V.; Kaggwa, W. S.
Relation: Computers and Geotechnics Vol. 36, Issue 3, p. 503-516
Publisher Link: http://dx.doi.org/10.1016/j.compgeo.2008.07.002
Publisher: Elsevier
Resource Type: journal article
Date: 2009
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.
Subject: bearing capacity; cohesive soil; limit analysis; multilayered cohesive material; strip footing; neural network
Identifier: http://hdl.handle.net/1959.13/806769
Identifier: uon:7218
Identifier: ISSN:0266-352X
Language: eng
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