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Construction litigation prediction system using ant colony optimization

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  • Thaveeporn Pulket
  • David Arditi

Abstract

The frequency of construction litigation has increased over the years, making litigation a costly and time-consuming activity. It is in the interests of all parties to a construction contract to avoid litigation. A tool (Ant Miner) is proposed to predict the outcome of construction litigation, hence encouraging the parties to settle out of court. Ant Miner, a rule-based classification system extracts classification rules by using ant colony optimization. It is used on 151 Illinois circuit court cases filed in the period 1987-2005. The prediction model is composed of data consolidation, attribute selection, classification and assessment. The results provide evidence that Ant Miner performs better than models used in earlier studies and that the rule sets discovered by this tool are highly interpretable, but that this tool suffers a great deal from noisy data. If the parties involved in a dispute have access to the proposed system that predicts the decision of the courts with higher accuracy and reliability than before, then they are expected to avoid litigation and settle out of court in order to save considerable time, money and aggravation.

Suggested Citation

  • Thaveeporn Pulket & David Arditi, 2009. "Construction litigation prediction system using ant colony optimization," Construction Management and Economics, Taylor & Francis Journals, vol. 27(3), pages 241-251.
    • Handle: RePEc:taf:conmgt:v:27:y:2009:i:3:p:241-251
    DOI: 10.1080/01446190802714781
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    Cited by:

    1. Peipei Wang & Yunhan Huang & Jianguo Zhu & Ming Shan, 2022. "Construction Dispute Potentials: Mechanism versus Empiricism in Artificial Neural Networks," Sustainability, MDPI, vol. 14(22), pages 1-23, November.

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