An application of fuzzy factor analysis for sustainable bridge maintenance and retrofit projects. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Yadollahi, Mohammadreza; Nazari, Rouzbeh; Spanos, Nicholas Joseph; Minner, Nicholas
• Is Part Of: International journal of management science and engineering management. Volume 12:Issue 4(2017)
• Page Start: 225
• Page End: 236
• Publication Date: 2017-10-02
• Subjects: Bridge -- maintenance and rehabilitation -- sustainable development -- factor analysis -- fuzzy set theory; Classification Methods -- Cluster Analysis -- Principal Components -- and Factor Models; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2016.1205528
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract: Sustainability in bridge maintenance and retrofit projects is an essential factor to achieve sustainable infrastructure development. Many studies have discussed issues relating to sustainable design and construction of transportation infrastructure; however, no major study has been conducted to identify the most significant factors involved in sustainable bridge rehabilitation. The aim of this study is to address the lack of Critical Green Factors (CGFs) for bridge rehabilitation projects. An extensive review was carried out to explore and identify the factors that are appropriate for rehabilitation projects. The research data were collected from a questionnaire survey and discussions with bridge engineering experts. Because of the imprecision and vagueness in subjective responses, the fuzziness in data analysis was taken into account by applying fuzzy set theory. The identified factors were then classified applying factor analysis to reduce the green factors into smaller meaningful groups. Three cut-off values, λ = 0.85, 0.90, and 0.95, were tested which resulted in the identification of 120, 111 and 77 factors, respectively. Accordingly, 77 critical factors were selected and classified together as a cluster of similar items into 14 groups. The findings of the study are expressed as groups of weighted CGFs that can be applied in green bridges rating systems.