1、Hierarchy probability cost analysis model incorporate MAIMS principle for EPC project cost estimation 4. Hierarchy integrated probability cost analysis (HIPCA) models for EPC cost estimation. In this section we introduce hierarchy probability cost analysis (HIPCA) methodology, which incorporates all
2、 aforementioned concepts for determining the total project cost (TPC) of EPC projects. Our objective is to develop an optimal but realistic TPC for a given probability of success (PoS) that we assume has been specified by allocating the baseline budgets, and managing contingency, based on the desire
3、 to win the project and risk tolerance. 4.1. Correlation coefficient and its feasible verification Once historical data is available, two different measures are used to reflect the degree of relation between cost elements in literature. The first one is an ordinary product-moment (Pearson) correlati
4、on coefficient and the second is a rank (Spearman) correlation coefficient. A non-parametric (distribution-free) rank statistic proposed by Spearman in 1904 as a measure of the strength of the associations between two variables ( Lehmann whereas positive values of the correlation coefficient tend to
5、 widen the total-cost probability distribution and thus increase the gap between a specific cost percentile (e.g., 70%) and the best-estimate cost. That is to say, the contingency could be larger. Therefore, using reasonable non-zero values, such as 0.2 or 0.3, generally leads to a more realistic re
6、presentation of total-cost uncertainty. Subjective judgment also finds application in specifying the cor-relations between cost elements qualitatively. To this respect, researchers can subjectively choose two groups of correlations to assess strong, moderate, and weak relations: 0.8,0.45,0.15 ( Touran, 1993) and 0.85,0.55,0.25 ( Chau, 1995). Other more recent scholars explain, simply, as a rule of thumb, we can say that correlations of less than 0.30 indicate little if any relationship bet
