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, KW 
Keywords: 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 How to cite?
Journal: Automation in construction 
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.
URI: http://hdl.handle.net/10397/989
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2006.11.008
Rights: Automation in Construction © 2007 Elsevier B.V. The journal web site is located at http://www.sciencedirect.com.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
AIC7.pdfPre-published version94.39 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

8
Last Week
0
Last month
0
Citations as of Apr 27, 2016

WEB OF SCIENCETM
Citations

8
Last Week
0
Last month
0
Citations as of May 1, 2016

Page view(s)

6
Last Week
0
Last month
Checked on May 1, 2016

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.