Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1272
Title: Predicting construction litigation outcome using particle swarm optimization
Authors: Chau, KW 
Keywords: Particle swarm optimization
Construction litigation outcome
Artificial intelligence technologies
Backpropagation
Artificial neural networks
Mathematical models
Decision making
Issue Date: 2005
Publisher: Springer-Verlag Berlin Heidelberg
Source: Lecture notes in artificial intelligence, 2005, v. 3533, p. 571-578 How to cite?
Abstract: Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show faster and more accurate results than its counterparts of a benching back-propagation neural network and that the PSO-based network are able to give a successful prediction rate of up to 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.
URI: http://hdl.handle.net/10397/1272
ISBN: 978-3-540-26551-1
DOI: 10.1007/11504894_80
Rights: © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.
Appears in Collections:Book Chapter

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