【正文】 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)] 。 and (3) user input to establish threshold PWPCE value to isolate potential risk attributes by using the DAM and for developing a project cost control strategy. Several logical checks have been provided throughout the system to assist the user with data entry and analysis. MODULE 1 The objective of module 1 is to extract information regarding the conditional relationship attributes and their relative cost influence. This information is calibrated for use in a new project, with respect to the subjective input provided by the team members regarding the new project characteristics. Module 1 is prised of two models (refer to Fig. 2), the DPM and the GDM. Data Processing Model (DPM) The DPM has two stages (refer to Fig. 3). The objective of stage 1 of the DPM is to analyze the past project performance data provided by the user. This analysis establishes the conditional probability of an attribute attaining active state, given that the attributes preceding it in the influence pattern have attained the active state. The conditional probabilities calculated in this model are calibrated in the GDM. The calibrated conditional probabilities are later used by the PWPCE model in module 2 to pute the active state probability of attributes in a new project. The individual cost influence of attributes in each historical project is puted in stage 2 of the DPM (refer to Fig. 3).The process starts by isolating the necessary information from a number of past projects (say, n). Two criteria are remended for selecting past projects。 and (3) module 3to develop a project cost control strategy to minimize the expected loss. FRAMEWORK OF COMPASS The accuracy of a system depends to a large extent on the validity 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