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Article type: Research Article
Authors: Fan, Ching-Lung; *
Affiliations: Department of Civil Engineering, The Republic of China Military Academy, Fengshan, Kaohsiung, Taiwan
Correspondence: [*] Corresponding author. Ching-Lung Fan, Ph.D., Department of Civil Engineering, the Republic of China Military Academy, No. 1, Weiwu Rd., Fengshan, Kaohsiung 830, Taiwan. E-mail: [email protected].
Abstract: Construction inspection is a crucial mechanism for evaluating the construction quality of public construction in Taiwan. Inspection scores are results based on the experiences or subjective evaluations of auditors. Although general rating principles and procedures are specified, objective standards are not adopted to determine scores and cannot concretely demonstrate actual construction quality. This study integrated the analytic network process (ANP) and fuzzy set (FS) to develop a construction quality index (CQI) model as a concrete indicator and objective standard for evaluating construction quality. Based on past defect data inspected by auditors, dependent factors for defects were established, and the ANP was employed to calculate the weights of all factors. Subsequently, the frequencies of defects for all items in a construction inspection were multiplied by the factor weights and summed to obtain a set of CQI values. Furthermore, the use of FS reasoning affects important defects (10 items) in project quality and important values (IV). The CQI values obtained were as follows. Grade A was lower than 1.0, Grade B was between 1.0 and 3.0, Grade C was between 3.0 and 5.0, and Grade D was greater than 5.0 (IV). The CQI model is based on the frequency of defects and weights, improves the problem of intuitive on-site ratings provided by auditors, and establishes an objective and simple method for rating construction quality to effectively enhance rating standards for construction inspections.
Keywords: Analytic network process, fuzzy set, construction quality index, construction inspection, grade
DOI: 10.3233/JIFS-190608
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3011-3026, 2020
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