Application of a PSO-based neural network in analysis of outcomes of construction claims

Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

246 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)642-646
Number of pages5
JournalAutomation in Construction
Issue number5
Publication statusPublished - 1 Aug 2007


  • Artificial neural networks
  • Construction claims
  • Particle swarm optimization

ASJC Scopus subject areas

  • Civil and Structural Engineering


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