A Multi-attribute Decision-Making to Sustainable Construction Material Selection: A Bayesian BWM-SAW Hybrid Model

Ramazan Alkan (), Melih Yucesan () and Muhammet Gul ()
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Ramazan Alkan: Munzur University
Melih Yucesan: Munzur University
Muhammet Gul: Munzur University

A chapter in Advances in Best-Worst Method, 2022, pp 67-78 from Springer

Abstract: Abstract The increase in urbanization and developments in the production industry has led to rapid progress in the construction sector. Many new strategies are developed in the industry to reduce costs and improve building quality. In recent years, the necessity of sustainable construction practices comes to the fore to renew the building stock damaged as a result of natural disasters and reduce the cost concerns that arise in this situation to a reasonable level. Due to limited resources and environmental concerns, researchers and practitioners have begun to develop sustainable building materials. The problem of selecting these materials when constructing a new building is vital. In particular, depending on the sector's rapid growth in Turkey, it is becoming more and more important to select the best sustainable construction material. Therefore, this paper proposes a model to evaluate the most appropriate sustainable construction material via two multi-attribute decision-making (MADM) methods called “Bayesian Best-Worst Method (BWM) and Simple Additive Weighting (SAW)”. Initially, the criteria derived from existing literature were evaluated with the aid of construction sector-based respondents and extra information about the interrelationship between the criteria were determined by credal ranking in Bayesian BWM. Then, via SAW, the most appropriate material was selected among a set of alternatives. Two cases regarding sustainable insulating material selection are considered for the demonstration of the proposed MADM model.

Keywords: Sustainable construction material; Bayesian BWM; SAW (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-89795-6_6

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