EPC contractor risk early-warning model based on principal component analysis and neural network

Abstract

Along with engineering projects scale's expansion and projects quantity's increase, the owner more and more use EPC (Engineering Procurement and Construction) contract management pattern to raise the efficiency, reduce costs and transfer the majority of the project risks to the contractors. EPC contractors face bigger risks whose factors are complex throughout the processes of design, procurement and construction. Therefore, risk controlling has become an important part of the EPC contractor management. If the risk rank can be identified as soon as it appeared, followed by appropriate measures taken by the contractor, the loss of the contractor can be substantially reduced. In this paper, we combine the methodology of principal component analysis and neural network to choose projects statistical data that the contractor has completed as training data of the neural network. Then the EPC contractor risk early-warning system could be established. This system could divide the rank of the risk among the project process into three grades: slight risk, medium risk, and heavy risk, so that the contractor could make accordingly decisions in time based on the result of this system. Abstract—This electronic document is a “live” template.