A split-step PSO algorithm in predicting construction litigation outcome

Abstract

Owing to the highly complicated nature and the escalating cost involved in construction claims, 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. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to predict the outcome of construction claims in Hong Kong. The advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the conventional PSO algorithm, it attains a higher accuracy in a much shorter time.