Predicting the technical reusability of load-bearing building components: A probabilistic approach towards developing a Circular Economy framework

Kambiz Rakhshanbabanari, Jean-Claude Morel, Alireza Daneshkhah

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)
    12 Downloads (Pure)

    Abstract

    The construction sector is the largest consumer of raw materials and accounts for 25%–40% of the total CO2 emissions globally. Besides, construction activities produce the highest amount of waste among all other sectors. According to the waste hierarchies, reuse is preferred to recycling; however, most of the recovery of construction and demolition wastes happens in the form of recycling and not reuse. Part of the recent efforts to promote the reuse rates includes estimating the reusability of the load-bearing building components to assist the stakeholders in making sound judgements of the reuse potentials at the end-of-life of a building and alleviate the uncertainties and perceived risks. This study aims to develop a probabilistic model using advanced supervised machine learning techniques (including random forest, K-Nearest Neighbours algorithm, Gaussian process, and support vector machine) to predict the reuse potential of structural elements at the end-of-life of a building. For this purpose, using an online questionnaire, this paper seeks the experts’ opinions with actual reuse experience in the building sector to assess the identified barriers by the authors in an earlier study. Furthermore, the results of the survey are used to develop an easy-to-understand learner for assessing the technical reusability of the structural elements at the end-of-life of a building. The results indicate that the most significant factors affecting the reuse of building structural components are design-related including, matching the design of the new building with the strength of the recovered element.
    Original languageEnglish
    Article number 102791
    Number of pages12
    JournalJournal of Building Engineering
    Volume42
    Early online date29 May 2021
    DOIs
    Publication statusPublished - 1 Oct 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Building Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Building Engineering, 42, (2021) DOI: 10.1016/j.jobe.2021.102791

    © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Funder

    The authors would like to thank Coventry University for funding this research project entitled “Recycling and Reuse of Construction and Building Materials in the Context of the Circular Economy.” Publisher Copyright: © 2021 Elsevier Ltd

    Keywords

    • Gaussian process
    • reuse
    • Building structure
    • Supervised machine learning
    • Random forest
    • K-nearest neighbours
    • Reuse

    ASJC Scopus subject areas

    • Mechanics of Materials
    • Safety, Risk, Reliability and Quality
    • Building and Construction
    • Civil and Structural Engineering
    • Architecture

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