TY - GEN
T1 - Predicting construction litigation outcome using particle swarm optimization
AU - Chau, Kwok Wing
PY - 2005/12/1
Y1 - 2005/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=26944447099&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 3540265511
SN - 9783540265511
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 571
EP - 578
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Innovations in Applied Artificial Intelligence, IEA/AIE 2005
Y2 - 22 June 2005 through 24 June 2005
ER -