Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Journal of Construction Engineering and Management, Volume 139, Issue 6 (June 2013).
Adel Awad(1), Aminah Robinson Fayek (2).
(1) Postdoctoral Fellow, Hole School of Construction Engineering, Dept. of Civil and Environmental Engineering, 1-050 Markin/CNRL Natural Resources Engineering Facility, Univ. of Alberta, Edmonton, Alberta, Canada T6G 2W2. E-mail: [email protected] and
(2) Professor, NSERC Industrial Research Chair in Strategic Construction Modeling and Delivery, Ledcor Professor in Construction Engineering, Hole School of Construction Engineering, Dept. of Civil and Environmental Engineering, 3-013 Markin/CNRL Natural Resources Engineering Facility, Univ. of Alberta, Edmonton, Alberta, Canada T6G 2W2 (corresponding author). E-mail: [email protected]
Abstract
The performance of a fuzzy expert system (FES) is significantly affected by the accuracy of its knowledge base parameters (membership functions and rule bases). The main contribution of this paper is in presenting a methodology to integrate an FES with adaptation/optimization techniques and applying the data-based adaptive learning concept to increase the accuracy of an FES developed for contractor default prediction for surety bonding. In addition, this paper investigates two optimization approaches (genetic algorithms and neural network back-propagation) for adaptation of fuzzy membership function (MBF) and rules’ degree of support (DoS) to determine the most suitable technique to adapt the FES. The optimized FES, called SuretyQualification, was validated using 30 hypothetical contractor default prediction cases, and the highest accuracy of the system (adapted using neural networks) was found to be 91.83%. Another contribution of this paper is the development of a software tool called SuretyQualification that provides a comprehensive and systematic evaluation process to evaluate a contractor and their risk of default on a project. The presented optimization approaches address FES context adaptation using any changing information conveyed by the input-output data and provide a methodology for continuous adaptation of the FES parameters, using practical cases to adjust the FES according to any contexts changes.
Additional Information
SuretyQualification software is developed based on decision-making model, for contractor default prediction. It is designed to conduct a comprehensive prequalification (surety underwriting) process that focuses not only on evaluating the contactor, but also on the proposed project aspects, the contractual risk, and the contractor’s organizational practices. SuretyQualification can be used for contractor evaluation/prequalification by surety underwriters, surety brokers, and owners in the construction industry. The software can also be used by contractors to conduct self-assessment to discover the areas that may cause them to default when performing a project and that need improvement.
