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Prioritizing Indicators for Material Selection in Prefabricated Wooden Construction

Author(s): ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Buildings, , n. 1, v. 14
Page(s): 63
DOI: 10.3390/buildings14010063
Abstract:

Material selection in buildings profoundly affects project success, encompassing durability, maintenance, customer satisfaction, production systems, lifecycle, usage, environment, and costs. Yet, there is a need for further research on indicators for choosing materials in prefabricated buildings. Therefore, this study’s main objective was to identify the indicators (criteria and sub-criteria) for selecting materials for prefabricated wooden construction and, subsequently, categorize these criteria and sub-criteria based on the perspective of industry professionals. To achieve this goal, three phases were carried out. First, a literature review was conducted to identify potential criteria for choosing structural and envelope materials in wooden prefabricated buildings. Second, a pilot survey was conducted in Canada and the United States to classify the priority order of the criteria obtained from the literature based on professionals’ opinions. Finally, Monte Carlo simulations were conducted with different iterations (1000, 10,000, and 100,000) using the data obtained from the previous phase to improve decision-making and classification processes. For the indicators to select materials, the literature review identified seven main criteria: performance properties, green materials, energy efficiency, circular economy, site conditions and material logistics, standards, and social impact. These criteria contained a total of 25 sub-criteria. The pilot survey data analysis demonstrated that the performance properties, site conditions and material logistics, and social impact criteria were consistently prioritized. The critical sub-criteria identified were fire resistance, watertightness, local availability, occupant health, and safety and protection. For the Monte Calo simulations, the predictions aligned with the pilot study, enhancing the robustness of the results.

Copyright: © 2023 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10753528
  • Published on:
    14/01/2024
  • Last updated on:
    07/02/2024
 
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