Dynamic sustainable success prediction model for infrastructure projects: a rough set based fuzzy inference system. Issue 4 (6th May 2020)

• Record Type: Journal Article
• Authors: Akbari, Saeed; Pour Rahimian, Farzad; Sheikhkhoshkar, Moslem; Banihashemi, Saeed; Khanzadi, Mostafa
• Is Part Of: Construction innovation. Volume 20:Issue 4(2020)
• Page Start: 545
• Page End: 567
• Publication Date: 2020-05-06
• Subjects: Infrastructure projects -- Rough set theory -- Sustainability -- Project success -- Fuzzy inference system; Construction industry -- Data processing -- Periodicals; Information technology -- Management -- Periodicals; 690.0285
• DOI: 10.1108/CI-04-2019-0034
• Languages: English
• ISSNs: 1471-4175

Abstract

Abstract : Purpose: Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria. Design/methodology/approach: To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the input for the rough set, which returned a set of rules as the output. The generated rulesets were then filtered in fuzzy inference system (FIS), before serving as the basis for the DSS. The developed prediction tool was tested and validated by applying data from a real infrastructure project. Findings: The results show that the developed rough set fuzzy method has strong ability in evaluation and prediction of the project success. Hence, the efficacy of the DSS is greatly related to the rule-based system, which applies RST to generate the rules and the result of the FIS was found to be valid via running a case study. Originality/value: Use of DSS for predicting the sustainable success of the construction projects is gaining progressive interest. Integration of RST and FIS has also been advocated by the seminal literature in terms of developing robust rulesets for impeccable prediction. However, there is no preceding study adopting this integration for predicting project success from the sustainability perspective. The developed system in this study can serve as a tool to assist the decision-makers to dynamically evaluate and predict the success of their own projects based on different sustainability criteria throughout the project life cycle.