Managing of Subcontractor Risks in Construction Projects

Abstract

In a typical building construction project, subcontracting accounts for around 40% of the total effort. Given this proportion, it would not be overstatement to say that serious dangers to the construction sector would arise if the issues related to substantial subcontracting are not addressed. This is because a subcontractor's project team's performance and excellence impact the project's outcome in terms of quality and timely delivery, which is crucial in determining a project's economic performance. The paper aims to merge risk management with subcontractor risks to manage these risks efficiently using artificially intelligent algorithms to maximize performance.  the methodology of this paper includes a questionnaire to collect subcontractors' risks and then build a system to manage these risks and reduce the effect on the project performance by using a gravitational search algorithm to classify the risk and find the optimal solution. The results show that the most problems relating to the subcontractor are quality as the subcontractor fails to fulfill the quality of the decided project and hence increases cost and time; GSA shows a very good algorithm in selecting the optimal solution for each risk, as for quality performance monitoring and feedback risk the algorithm select the response is to avoid the risk by fitness 0.89 as conclusion the subcontractors have main effect on the project performance as they are responsible more than 30% of the tasks and any failure will lead to inadequate projects.