Prediction of outcome of construction dispute claims using multilayer perceptron neural network model

The occurrence of disputes in Indian construction contracts results in damaging the relationship between the parties apart from the time and cost overruns. However, if the parties to a dispute can predict the outcome of the dispute with some certainty, they are more likely to settle the matter out of court resulting in the avoidance of expenses and aggravation associated with adjudication. Dispute resolution process is mainly based upon the facts about the case like conditions of the contracts; actual situations on site; documents presented during arbitrational proceedings, etc., which are termed as 'intrinsic factors' in this research. These facts and evidences being intrinsic to the cases have been explored by researchers to develop dispute resolution mechanisms. This study focuses on determining the intrinsic factors for construction disputes related to claims raised due to variation from 72 arbitration awards through Case Study approach and furthermore statistically proving their importance in arbitral decision making by seeking professional cognizance through a questionnaire survey. It also further asserts the feasibility of the multilayer perceptron neural network approach based on the intrinsic factors existing in the construction dispute case for predicting the outcome of a dispute. Data from 204 variation claims from the awards is employed for developing the model. A three-layer multilayer perceptron neural network was appropriate in building this model, which has been trained, validated, and tested. The tool so developed would result in dispute avoidance, to some extent, and would reduce the pressure on the Indian judiciary. (C) 2015 Elsevier Ltd. APM and IPMA. All rights reserved.