Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1280
Title: Prediction of construction litigation outcome using a split-step PSO algorithm
Authors: Chau, KW 
Keywords: Particle swarm optimization
Construction litigation outcome
Artificial intelligence technologies
Algorithms
Backpropagation
Artificial neural networks
Cost effectiveness
Mathematical models
Convergence of numerical methods
Decision making
Issue Date: 2006
Publisher: Springer Berlin / Heidelberg
Source: Lecture notes in computer science, 2006, v. 4233, p. 1101-1107 How to cite?
Abstract: The nature of construction claims is highly complicated and the cost involved is high. It will be advantageous if the parties to a dispute may know with some certainty how the case would be resolved if it were taken to court. The recent advancements in artificial neural networks may render 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 split-step particle swarm optimization (PSO) model is applied to train perceptrons in order to predict the outcome of construction claims in Hong Kong. It combines the advantages of global search capability of PSO algorithm in the first step and the local convergence of back-propagation algorithm in the second step. It is shown that, through a real application case, its performance is much better than the benchmark backward propagation algorithm and the conventional PSO algorithm.
URI: http://hdl.handle.net/10397/1280
ISBN: 978-3-540-46481-5
DOI: 10.1007/11893257_120
Rights: © Springer-Verlag Berlin Heidelberg 2006. The original publication is available at http://www.springerlink.com.
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