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 significantly.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Innovations in Applied Artificial Intelligence, IEA/AIE 2005|
|Period||22/06/05 → 24/06/05|
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science