[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127523-en":3,"doc-seo-127523-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127523,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Stakeholders’ impact on the reuse potential of structural elements at the end-of-life of a building - A machine learning approach","The building and construction sector consumes the largest share of nonrenewable resources and generates substantial waste and greenhouse-gas emissions. Because embodied energy and CO2 during construction and demolition are largely tied to building structure, extending component service life is prioritized. This study provides practitioner-friendly instructions to assess social sustainability and responsibility when reusing load-bearing structural elements. Results from advanced supervised machine learning indicate regulatory authorities’ perception as the dominant social factor, with risks ranking next and affecting perception via strong correlation, supported by a Bayesian network capturing non-linear relationships for reliable estimation of social reusability.","Journal of Building Engineering 70 (2023) 106351  \nContents lists available at ScienceDirect  \nJournal of Building Engineering  \njournal [homepage:](homepage: www.elsevier.com/locate/jobe)[ www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe)  \n| Stakeholders’ impact on the reuse potential of structural elements   at the end-of-life of a building: A machine learning approach\u003Cbr>Kambiz Rakhshana, *, Alireza Daneshkhahb, Jean-Claude Morelc\u003Cbr>a Leeds Sustainability Institute, Leeds Beckett University, Northern Terrace, Woodhouse Lane, Leeds, LS2 8AG, United Kingdom\u003Cbr>b Research Centre for Computational Science and Mathematical Modelling, Coventry University, Priory Street, CV1 5FB, Coventry, United Kingdom c Laboratoire de Tribologie et Dynamiques des Systemes, ENTPE, Universite de Lyon, France |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Bayesian network Building structural elements Experts' elicitation\u003Cbr>Social reusability\u003Cbr>Supervised machine learning techniques |  | The construction industry, and at its core the building sector, is the largest consumer of nonrenewable resources, which produces the highest amount of waste and greenhouse gas emissions worldwide. Since most of the embodied energy and CO2 emissions during the construction and demolition phases of a building are related to its structure, measures to extend the service life of these components should be prioritised. This study develops a set of easy-to-understand instructions to facilitate the practitioners in assessing the social sustainability and responsibility of reusing the load-bearing structural components within the building sector. The results derived by developing and then employing advanced machine learning techniques indicate that the most significant social factor is the perception of the regulatory authorities. The second and third ranks among the social reusability factors belong to risks. Since there is a strong correlation between perception and risk, the potential risks associated with reusing structural elements affect the stakeholders’ perception of reuse. The Bayesian network developed in this study unveil the complex and non-linear correlation between variables, which means none of the factors could alone determine the reusability of an element. This paper shows that by using the basics of probability theory and combining them with advanced supervised machine learning techniques, it is possible to develop tools that reliably estimate the social reusability of these elements based on influencing variables. Therefore, the authors propose using the developed approach in this study to promote materials' circularity in different construction industry sub-sectors. |\n\n1. Introduction  \nThe construction industry is the backbone of the economic growth of many countries worldwide. With a global value of $11.6 trillion by 2030 and a Gross Domestic Product (GDP) of up to 10.5% in European countries [1], it employs nearly 7.8% of the total labour force in the UK [2–4]. However, this considerable contribution to the global economy makes this sector a leader in undesirable areas such as non-renewable resources consumption, waste and greenhouse gas (GHG) emissions production [5–12]. Therefore, it is inevitable to take efficient measures to improve the overall sustainability of the construction sector to maintain the rise of the global temperature below 2 °C and comply with the requirements of the Paris agreement and COP27 [13]. According to the signatories of the Paris Agreement, one subsidiary of the construction industry that has a high potential to take part in this venture is the building  \n* Corresponding author.  \nE-mail addresses: [k.rakhshanbabanari@leedsbeckett.ac.uk](k.rakhshanbabanari@leedsbeckett.ac.uk), [kambizking@yahoo.com](kambizking@yahoo.com) (K. Rakhshan), [Ali.Daneshkhah@coventry.ac.uk](Ali.Daneshkhah@coventry.ac.uk) (A. Daneshkhah), [JeanClaude.MOREL@entpe.fr](JeanClaude.MO","cbCaipx2loKmzyqA","https://ap.wps.com/l/cbCaipx2loKmzyqA","pdf",1891848,2,1,16,"English","en",105,"# Introduction\n## Reuse and sustainability challenges in construction\n## Prior work on estimating structural reuse potential\n## Gap: social and economic factor impacts","[{\"question\":\"Why focus on reusing load-bearing structural elements in buildings?\",\"answer\":\"Most embodied energy and CO2 emissions during construction and demolition are linked to the building’s structure, so extending the service life of structural components supports sustainability. Waste hierarchies also prioritize reuse as an effective solution.\"},{\"question\":\"Which stakeholder-related factor is identified as most significant for social reusability?\",\"answer\":\"The study finds that the perception of regulatory authorities is the most significant social factor affecting social reusability of structural elements.\"},{\"question\":\"How do risks influence stakeholders’ perception of reuse?\",\"answer\":\"Risks are ranked second and third among social reusability factors. The results show a strong correlation between perception and risk, meaning potential risks associated with reuse affect stakeholders’ perceptions.\"}]","Stakeholders’ impact on the reuse potential of structural elements at the end-of-life of a building - A machine learning approach | PDF",1785939734,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"stakeholders-impact-on-the-reuse-potential-of-structural-elements-at-the-end-of-life-of-a-building-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/stakeholders-impact-on-the-reuse-potential-of-structural-elements-at-the-end-of-life-of-a-building-a-machine-learning-approach/127523/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why focus on reusing load-bearing structural elements in buildings?","Question",{"text":76,"@type":77},"Most embodied energy and CO2 emissions during construction and demolition are linked to the building’s structure, so extending the service life of structural components supports sustainability. Waste hierarchies also prioritize reuse as an effective solution.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which stakeholder-related factor is identified as most significant for social reusability?",{"text":81,"@type":77},"The study finds that the perception of regulatory authorities is the most significant social factor affecting social reusability of structural elements.",{"name":83,"@type":74,"acceptedAnswer":84},"How do risks influence stakeholders’ perception of reuse?",{"text":85,"@type":77},"Risks are ranked second and third among social reusability factors. The results show a strong correlation between perception and risk, meaning potential risks associated with reuse affect stakeholders’ perceptions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]