Risk Assessment for Construction Projects Contracting Based on Unascertained Sets

This paper utilizes unascertained sets to analyze general contracting risk of construction projects. Firstly, principal component method was used to process a number of listed general contracting risk indices. The representative indices from principal component analysis process substitute for the primary indexes. Thus subjective random problem in choosing indices can be avoided. Then, risk factor weights are qualitatively described with information entropy, and the qualitative results are transformed to quantities value and the result of evaluation is worked out by using unascertained number algorithm. Thirdly, Using the theory and method of unascertained measure, a novel unascertained C-means clustering model and the clustering weight ale established to determine the degrees of the risk of general contracting. Finally, a case study was carried out on the risk assessment of sample projects using the prototype. The results show that unascertained sets can help understand the uncertainties in construction contracting risk assessment, and the relationships between risk sources and the consequences on project performance measures can be identified and quantified consistently.