Knowledge-based DSS for construction contractor prescreening

This paper presents the development of a knowledge-based decision support system for predicting construction contract bond claims using contractor financial data. The learning and refining sub-system of the proposed DSS employs Inductive Learning and Neural Networks to extract the problem solving knowledge to catch the contractor's deteriorating financial condition. The acquired knowledge is stored in the knowledge sub-system and continually updated to incorporate recent additional information. This acquired knowledge augments the existing statistical models including multiple discriminate analysis, regression, and logistic regression models. We propose a framework for integrating fragmented models and knowledge into a DSS so that sureties can analyze the outcome of each model and knowledge in what-if manner. Moreover, proposed DSS is equipped with the meta-knowledge selecting the most suitable models and knowledge for the given situation intelligently thus providing peer-opinion for the sureties.

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