【正文】 of the input data provided by the user. Therefore, it is important to properly analyze past project performance data before the data are used in identifying the potential risk attributes and in developing a project cost control strategy for a new project. The DPM was developed to assist the user in this aspect and to isolate the necessary information from the available past project performance data. DPM However, since every construction project is unique, the historical data cannot be used in analyzing a new project without giving proper consideration to the new project characteristics. The GDM was developed to take into account this important aspect and to calibrate the past project performance data (as analyzed in the DPM) before the data are used in analyzing a new project. The calibration is performed by soliciting subjective input from the team members with respect to the unique characteristics of the new project (refer to Fig. 2). The PWPCE model assists the user in calculating the probability of an attribute influencing the cost of a project and also the percentage cost escalation (with respect to the estimated project cost) due to that influence. This model utilizes the input provided by the DPM and the GDM to calculate the expected percentage cost escalation in a new project and also the individual cost influence of attributes in that output of the PWPCE model (., the individual cost influence of attributes and their probability of influencing the project cost) is then utilized by the DAM to formulate a cost control strategy for the new project. The puterization of the COMPASS methodology has eliminated the need for the user to follow the flow of information within the modules. To apply the COMPASS methodology, the user interaction with the system is limited to the decision making points, while the data analysis and putations are performed by the system. The user interaction with the puterized system is required at the following instances: (1) relevant data extraction from the past project performance data (to be used in the DPM)。 and (2) they should have faced a cost escalation. For each historical project, the user subjectively identifies the state of attributes by using a binary mode, as explained earlier under the modeling concepts (refer to part B of Fig. 3). This information about the state of attributes in historical projects is processed by the DPM to determine The conditional probabilities, ., p(C =llA =1), p(E = 11 C = 1), and so forth [refer to part A of Fig. 3 and (2) and (3)] The individual cost influence of attributes (refer to part C of Fig. 3) The conditional probabilities are further calibrated in the GDM. This calibration is done with respect to the new project characteristics. The calibrated conditional probabilities and the individual cost influence of attributes are used as an input for the PWPCE model in module 2 to analyze a new project. p(C =llA =1) =p[(C =1) n (A =1)] i p(A =1) (2) p(C =llA =1) =2: [(C =1) and (A =1)]/ i 2: (A =1)/ where j = 1 ... n (n = number of past projects selected) (3) In the second stage of the DPM, a significant level of escalation is defined for each historical project (Le., a level of escalation that was accounted for in the contingency fund for the project). The critical line items and their associated escalation values are identified by considering the significant level of escalation thus defined (refer to part C of Fig. 3). Critical line items are those line items that had faced a cost escalation greater than the significant level of escalation defined for that project. Each critical line item is associated with a quantifiable attribute. For example, (refer to part C of Fig. 3), critical line item number 1 is associated with quantifiable attribute X (say, total labor cost) and critical line item number 2 is associatedwith quantifiable attribute Y (say, total material cost). After establishing the association between the critical line items and the quantifiable attributes, the user analyzes each critical line item with respect to the list of attribute analysis is conducted to subjectively identify attributes (or attribute relationships) that could have influenced that particular line item, thus making it critical. Again a binary mode (1 =yes, and 0 =no) is used to define the subjective relationships. For instance, (refer to part C of Fig. 3), critical line item number 1 could have been influenced by attribute A and attribute relationship D IA (., the active state of attribute D due to the influence of its preceding attribute A). As an example, consider the influence of a remote site location (attribute A) on availability of resources (attribute D) and their collective impact on the labor cost (attribute X) of a critical line item j (say, structural steel erection). Similar analysis is done for each of the n historical projects by the user or the person(s) knowledgeable about the selected projects. The DPM putes the individual cost influence of attributes per historical project. It utilizes the analysis done by the user and the data with respect to the state of attributes obtained in stage 1 (refer to part B of Fig. 3). This information is later used by the PWPCE model of module 2 to determine the relative cost influence of attributes and their cost influence in a new project. The individual cost influence of attributes in a particular project is puted as shown for line item number 1 in (4)(11) (refer to part C of Fig. 3). Line Item Number 1 Influenced by Attributes A and DIA (Refer to Part C of Fig. 3) p(X = 11A = 1) = p[(X = 1) n (A = 1)] 。 {p(X = llA = 1) + p(X = 11A = 1, D = I)}] X (CE)t (8) CI(DIA)t = [p(X = 11A = 1, D = 1) 。 and (2) the individual cost influence of attributes. The importan