Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques

Abstract

The construction project is subject to several risks, one of the most important of which is contractor default because contractor default may increase the final project cost considerably. In the US construction industry, owners commonly shield themselves from the risk of contractor default by transferring this risk to the contractor, who in turn transfers this risk to a surety company. On the other hand, the General Directorate of Military Works (GDMW) of the Kingdom of Saudi Arabia retains the risk of contractor default rather than transferring it to a third party. An artificial neural network (ANN) and a genetic algorithm (GA) are used in this study to predict the risk of contractor default in construction projects undertaken for the Saudi armed forces. Based on this prediction, the Saudi GDMW can make a decision to engage or not to engage the services of a contractor. In case the models are not able to generate reliable predictions (or generate contradictory outcomes), the GDMW will have to augment its...