Study On Progress Risk Evaluation Based On Improved Monte Carlo

As a carrier of the national fixed asset investment, the project has a positive effect to promote national economic growth and maintain national economic long-term prosperity. The project construction has a big investment, long cycle and complex structure, whether it can achieve the desired results is a process full of uncertainty. In its construction, there are many risk factors, the relationship between these factors are also complicated. Some risk factors may cause the change of activity duration, resulting in the total project duration delay. Therefore, it is necessary to evaluate the progress risk before the implementation of the project, which is of great significance for guaranteeing the successful completion of the project construction.In the practical project, there are a number of risk factors and their distribution types are different. Most of the progress evaluations usually consider that risk factors are the average distribution throughout the period of project construction, and the risk factors have not broken down into the activity duration. In addition, when using traditional Monte Carlo method to evaluate progress risk, the activity duration is regarded as a separate random variable, ignoring the impact of risk factors. This paper tries to find out the relationship between the risk factors and activity duration and improve the conventional Monte Carlo method.The research object of this paper is the progress of the project. Considering the impact of risk factors on activity duration, the risk factors of different probability distribution is introduced to compute the activity duration. Put the sampling formula of activity duration under the influence of risk factors into the Monte Carlo simulation system, establishing the progress risk model based on improved Monte Carlo method. In addition, based on the improved model, propose the micro and macro quantitative index-ACP, ACI,ACCI, PCCI.Finally, evaluate the progress risk of a construction project by using the improved Monte Carlo simulation model. Compared with the classical CPM/PERT and traditional Monte Carlo method, we can find that using the improved model to forecast the progress makes the prediction result more accurate and more close to meet with actual engineering. Through the above analysis, confirmed the validity and practicability of the method in this paper.