AbstractThe reuse of building components can decrease the embodied energy and greenhouse gases of the construction activities and help get closer to a circular economy using fewer virgin materials. 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 develops probabilistic models using advanced supervised machine learning techniques to predict the reuse potential of structural elements at the end-of-life of a building, from technical, economic, and social perspectives.
After performing a thorough literature search and identifying, analysing, and categorising the independent variables affecting the reusability of building structural elements, these factors were used to develop an online questionnaire. This questionnaire was then shared with a representative sample of practitioners in the construction industry, including managers, CEOs, architects, engineers, consultants, and deconstruction experts with previous experience in reusing recovered building structural components. The received questionnaires were reviewed, and the initial dataset was split into three separate datasets to address the technical, economic, and social aspects of the study. Then, the missing values were estimated, and the class imbalances were addressed using advanced techniques. In the next stage, and for each dataset, a total number of thirteen predictive models were developed in the R software using 13 advanced supervised machine learning methods. The performance and transparency of these models were compared to choose the best-practice Building Structural Elements Reusability Predictive Models (BSE-RPMs), which provide reliable predictions.
Random Forest (RF) models were selected as the best practice BSE-RPMs for all three datasets, with a considerable overall accuracy of 96%, 89%, and 94% for the technical, economic, and social models, respectively. Since RF models are known as black-box models, advanced supervised machine learning methods such as sensitivity analysis and visualisation techniques were employed to open the selected RF BSE-RPMs. Eventually, using advanced rule extraction methods, three easy-to-understand predictive models (learners) were developed for assessing the technical, economic, and social reusability of the load-bearing building components, with an overall accuracy of 85%, 82%, and 91%, respectively.
This research has contributed to promoting the reuse of building structural elements in two ways. First, using advanced supervised machine learning techniques such as the Boruta method and recursive feature elimination technique, this research identifies and ranks the main reusability factors based on the experience of the stakeholders with the recovered building structural elements in the building sector. Second, for the first time, it develops three sets of easy-to-understand learners (predictive rules) that can be used by practitioners to have an initial assessment of the technical, economic, and social reusability of the load-bearing components. The developed learners can be easily used by various stakeholders and have the potential to promote the reuse rate of the structural elements of the existing buildings, which were not designed for deconstruction. These sets of rules can also encourage more deconstruction projects since the developers would have a better judgment about the reusability of the structure of an existing building at its end-of-life, which, in turn, can accelerate the growth of reuse markets.
|Date of Award||Jul 2021|
|Supervisor||Alireza Daneshkhah (Supervisor), Messaoud Saidani (Supervisor), Hafiz Alaka (Supervisor) & Jean-Claude Morel (Supervisor)|