Predicting the outcome of construction litigation using neural networks

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Arditi D., Oksay F. E., Tokdemir O. B.

Computer-Aided Civil and Infrastructure Engineering, vol.13, pp.75-81, 1998 (SCI-Expanded) identifier


In this study, neural networks were used to predict the outcome of construction litigation. Disagreements between the owner and the contractor can arise from such considerations as interpretation of the contract, changes made by the owner, differing site conditions, acceleration and suspension of work, and so forth. When there are disagreements between the contractor and the owner, the result is the inefficient use of resources and higher costs for both the owner and the contractor, as well as damage to the reputation of both sides. Neural networks may help to predict the outcome of construction claims that are normally affected by a large number of complex and interrelated factors. Data composed of characteristics of cases and circuit and appellate court decisions were extracted from cases filed in Illinois appellate courts in the last 12 years. A network was trained using these data, and a rate of prediction of 67% was obtained. If the parties to a dispute know with some certainty how the case would be resolved if it were taken to court, it is believed that the number of disputes could be reduced greatly.