A probabilistic predictive model for assessing the economic reusability of load-bearing building components: Developing a Circular Economy framework

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2 Citations (Scopus)

Abstract

The reuse of load-bearing building components has the potential to promote the circular economy in the building sector. One recent aspect of the efforts to improve reuse rates in buildings is estimating the reusability of the structural elements. This study develops a probabilistic predictive model using advanced supervised machine learning methods to evaluate the economic reusability of the load-bearing building elements. The results of sensitivity analysis and visualization techniques used in this study reveal that the most important economic factor is the need to purchase reused elements early in a project, which could have cash flow implications. The other most important factors are the potential financial risks, the procurement process, and the labour cost. This study unveils that the relationship between variables is not linear, and none of the identified factors could alone determine if an element is reusable or not. This study concludes that the complex interdependencies of factors affecting reuse cause a high level of uncertainty about the feasibility of reusing the load-bearing building structural components from an economic aspect. Nonetheless, this paper reveals that by using the probability theory foundations and combining it with advanced supervised machine learning methods, it is possible to develop tools that could reliably estimate the economic reusability of these elements based on affecting variables. Therefore, the authors suggest utilizing the approach developed in this research to promote the circularity of materials in different subsectors of the construction industry.
Original languageEnglish
Pages (from-to)630-642
Number of pages13
JournalSustainable Production and Consumption
Volume27
Early online date26 Jan 2021
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Building structure
  • Economic reusability
  • Gaussian process regression
  • K-nearest neighbors
  • Random forest
  • Supervised machine learning

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Renewable Energy, Sustainability and the Environment
  • Industrial and Manufacturing Engineering

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