Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/989
Title: Application of a PSO-based neural network in analysis of outcomes of construction claims
Authors: Chau, Kwok-wing
Subjects: Particle swarm optimization
Artificial neural networks
Construction claims
Issue Date: Aug-2007
Publisher: Elsevier B.V.
Source: Automation in construction, Aug. 2007, v. 16, no. 5, p. 642-646.
Abstract: It is generally acknowledged that construction claims are highly complicated and are interrelated with a multitude of factors. It will be advantageous if the parties to a dispute have some insights to some degree of certainty how the case would be resolved prior to the litigation process. By its nature, the use of artificial neural networks (ANN) can be a cost-effective technique to help to predict the outcome of construction claims, provided with characteristics of cases and the corresponding past court decisions. This paper presents the adoption of a particle swarm optimization (PSO) model to train perceptrons in predicting the outcome of construction claims in Hong Kong. It is illustrated that the successful prediction rate of PSO-based network is up to 80%. Moreover, it is capable of producing faster and more accurate results than its counterparts of a benchmarking back-propagation ANN. This will furnish an alternative in assessing whether or not to take the case to litigation.
Rights: Automation in Construction © 2007 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/989
DOI: 10.1016/j.autcon.2006.11.008
ISSN: 0926-5805
Appears in Collections:CEE Journal/Magazine Articles

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