Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/918
Type: Journal article
Title: Artificial neural network-based settlement prediction formula for shallow foundations on granular soils
Author: Shahin, M.
Jaksa, M.
Maier, H.
Citation: Australian Geomechanics, 2002; 37(4):45-52
Publisher: Australian Geomechanics Society
Issue Date: 2002
ISSN: 0818-9110
Statement of
Responsibility: 
Mohamed A. Shahin, Mark B. Jaksa and Holger R. Maier
Abstract: The problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. The geotechnical literature has included many formulae that are based on several theoretical or experimental methods to obtain an accurate, or near-accurate, prediction of such settlement. However, these methods fail to achieve consistent success in relation to accurate settlement prediction. Recently, artificial neural networks (ANNs) have been used successfully for settlement prediction of shallow foundations on granular soils and have been found to outperform the most commonly-used traditional methods. This paper presents a new hand-calculation design formula for settlement prediction of shallow foundations on granular soils based on a more accurate settlement prediction from an artificial neural network model. The design formula presented is a quick tool from which settlement can be calculated easily without the need for computers.
RMID: 0020022102
Published version: http://www.ecms.adelaide.edu.au/civeng/staff/pdf/AusGeo2002_ANN_Equation.pdf
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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