【正文】 plied to the real EPC project to demonstrate its practical use. The Beta Pert and Weibull distribution are selected, respectively, for WBSitem cost element based on Section 3. The PDF and relative calibration for WBS level 4 (as Section 3) have done at the beginning of bidding stage with an applicable data set derived from historical cost and expert’s experience. Subjective correlation coefficient method is remended, and correlation group will be divided based on WBS level 2. The correlation coefficient of pairwise between cost elements within same group will be assigned as as initial coefficients. The correlation coefficient of pairwise between cost elements in a different group will be assigned as as initial coefficients. The consistency and feasibility of the correlation will be judged and adjusted permanently by Crystal Ball automatically as we point out in Section 4. . Results for four kinds of cost estimation models for a real bidding EPC project. A simulation experiment is designed to implement the proposed method and to evaluate the effects of the hierarchy integrated probability cost analysis model. In the experiment, five kinds of cost analysis models are adopted for assessment. The output statistics can then be used to assess the behavior of the true project cost and the effectiveness of the hierarchy integrated PCA model. . Typical PCA model All the cost elements and their margin/percentile distributions are shown in Table 1 for typical PCA. The value of each WBS level 3 cost elements is expressed as kilo euro. This level of granularity is suitable for typical PCA model. Moreover, the WBS level 3 can be changed to reflect the actual situation based on the size of the project and accuracy of the estimation, if the proposed method is applied to other construction projects. After 5000 simulation trials, the 6th column in Table 2 lists the descriptive statistics for the total cost of the project. To assess the impact of correlations, we pare two scenarios: including and excluding correlations. 5th column in Table 2 is the result of total project cost including correlations. The first observation is that both distributions are skewed to the right because the mean is larger than the median. The second observation is that the scenario of ‘‘including correlations’’ has a larger variability (uncertainty) than ‘‘excluding correlation’’. This conclusion is unsurprising because the former has a much greater standard deviation that the latter ( vs. , a 35% difference). To select typical down WBS level, the sensitivity analysis is executed and shown in Fig. 4. It is apparent that the unit 13 is more sensitive for the total cost of the project than other units. Unit 13 is selected as the 2nd hierarchy for the HPCA cost model. . Hierarchy PCA model excluding MAIMS principle All the cost elements and their margin/percentile distributions for the unit 13 are shown in Table 3. The second hierarchy probability distribution will be generated after 5000 simulation trials, and listed in Table 4 for details. The new distributions will substitute the corresponding distribution in Table 1. The descriptive statistics for the total cost of the project based on the hierarchy PCA model excluding MAIMS principle, are indicated in the 4th column in Table 2 after 5000 simulation trials. The standard deviation of the hierarchy model is not smaller than typical PCA, that is to say, the presented hierarchy model has overe the impact of increasing cost elements for cost risk analysis. . Hierarchy PCA model including MAIMSPDFs The descriptive statistics to estimate the total cost of the project are based on the hierarchy PCA model integrating MAIMS PDF is shown in the 3rd column in Table 2 after 5000 simulation trials. . Hierarchy PCA model including hierarchy MAIMSPDFs The descriptive statistics for the total cost of the project are based on the hierarchy PCA model integrating MAIMS hierarchy is shown in the 2nd column in Table 2 after 5000 simulation trials. . Comparison and validation In this section, we validate whether the proposed methods can solve the dilemma to appropriate cost elements and maximize the efficiency of cost information for EPC project. Probability of success (PoS) and confidence internal will be adopted to verify the quality of the estimation. The Monte Carlo simulation result of hierarchy PCA model integrating MAIMSPDFs is expressed in Fig. 5. The 10% and 90% points of the total cost of the project are based on hierarchy PCA model integrating MAIMSPDFs that establish a 80% confidence interval, and the PoS is generally expressed in percentages of +%/ %. The Monte Carlo simulation result of hierarchy PCA model integrating MAIMS hierarchy can be expressed as Fig. 6. The 10% and 90% points of the total cost of the project are based on hierarchy PCA model