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Risk assessment is the most difficult and most time-consuming process of project risk management. In the complex and volatile market environment, the project risk is more difficult to predict. Although numerous projects which have been completed provide much valuable knowledge for reference, it has not been put into good use. Thus, it is urgent to study how to apply the completed projects to help new projects avoid risks effectively. Therefore, this paper proposes the two-stage model of project risk assessment, in which decision tree is applied for discovering the risk rules at the first stage, and case-based reasoning for utilizing the tacit knowledge at the second stage. The model consists of the following four parts: (1) to explore risks and association rules from the project risk cases by using decision tree technology; (2) to use and evaluate rules; (3) to obtain assessment results by using case-based reasoning method; (4) to extend case base and rule base through case study and rule learning. The preliminary tests show that the model can assess project risks more quickly and accurately and the assessment results are accurate and viable.

Research on Two-Stage Model of Project Risk Assessment Based on Knowledge Discovery