Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/16732
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Type: Journal article
Title: Neural network based stochastic design charts for settlement prediction
Author: Shahin, M.
Jaksa, M.
Maier, H.
Citation: Canadian Geotechnical Journal, 2005; 42(1):110-120
Publisher: Natl Research Council Canada
Issue Date: 2005
ISSN: 0008-3674
1208-6010
Statement of
Responsibility: 
M A Shahin, M B Jaksa, H R Maier
Abstract: Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.
Keywords: shallow foundations; neural networks; Monte Carlo; stochastic simulation; settlement prediction
RMID: 0020050190
DOI: 10.1139/T04-096
Published version: http://pubs.nrc-cnrc.gc.ca/cgi-bin/rp/rp2_abst_e?cgj_t04-096_42_ns_nf_cgj
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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