Sciweavers

Share
IEAAIE
2005
Springer

Predicting Construction Litigation Outcome Using Particle Swarm Optimization

9 years 9 months ago
Predicting Construction Litigation Outcome Using Particle Swarm Optimization
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 backpropagation 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 s...
Kwok-wing Chau
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where IEAAIE
Authors Kwok-wing Chau
Comments (0)
books