Construction projects are complicated in nature and require many considerations in contractor selection. One of the complicated interactions is that between performance with the project size, and contractor financial status, and size of projects contracted. At the prequalification stage, the financial requirements restrict the contractors to meet minimum limits in financial criteria such as net worth, working capital and annual turnover, etc. In construction projects, however, there are cases when contractors meet these requirements but show low performance in practice. The model used in the study predicts the performance by training of a neural network. The data used in the study are 72 of the most recent roadwork projects in Bahrain. The results are shown in terms of the sensitivity of changing one variable on the performance of all the 72 projects. These results can reflect on the methods currently used on contractors’ assessments in the tendering stage and support decision-makers in assessing contractors and selecting the best bidders.
