Prediction of construction litigation outcome using a split-step PSO algorithm

Kwok Wing Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


The nature of construction claims is highly complicated and the cost involved is high. It will be advantageous if the parties to a dispute may know with some certainty how the case would be resolved if it were taken to court. The recent advancements in artificial neural networks may render 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 split-step particle swarm optimization (PSO) model is applied to train perceptrons in order to predict the outcome of construction claims in Hong Kong. It combines the advantages of global search capability of PSO algorithm in the first step and the local convergence of back-propagation algorithm in the second step. It is shown that, through a real application case, its performance is much better than the benchmark backward propagation algorithm and the conventional PSO algorithm.
Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540464816, 9783540464815
Publication statusPublished - 1 Jan 2006
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, Hong Kong
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4233 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Neural Information Processing, ICONIP 2006
Country/TerritoryHong Kong
CityHong Kong

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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