An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach
Construction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Recycling |
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| Online Access: | https://www.mdpi.com/2313-4321/10/3/115 |
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| author | Weihan Sun Quddus Tushar Guomin Zhang Andy Song Lei Hou Jingxuan Zhang Shuxi Wang |
| author_facet | Weihan Sun Quddus Tushar Guomin Zhang Andy Song Lei Hou Jingxuan Zhang Shuxi Wang |
| author_sort | Weihan Sun |
| collection | DOAJ |
| description | Construction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However, existing research lacks standardised estimation methods, the integration of digital technologies, and comprehensive lifecycle analysis approaches, limiting the effectiveness of waste prediction and management strategies. This study addresses the gap by conducting a scientometric analysis using CiteSpace and SciMAT, examining research trends, thematic clusters, and knowledge evolution in CDW quantification and management from 2014 to 2024. It establishes a conceptual framework for integrating digital systems and sustainable practices in CDW, focusing on waste generation rate, carbon emission, and phase-based waste management analysis. Network cluster analysis reveals the integral role of estimation tools and modelling techniques in refining waste generation quantification for building constructions. It also examines the interplay of digital tools, their influence on environmental cost reduction, and factors affecting waste production and environmental protection across project phases. This conjugate approach highlights the importance of the successful implementation of waste quantification and the imperative of machine learning for further investigation. This review offers an evidence-based framework to identify key stakeholders, guide future research, and implement sustainable waste management policies. |
| format | Article |
| id | doaj-art-5efd82ceb5534a0c8fa762a397957fcd |
| institution | Kabale University |
| issn | 2313-4321 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Recycling |
| spelling | doaj-art-5efd82ceb5534a0c8fa762a397957fcd2025-08-20T03:29:40ZengMDPI AGRecycling2313-43212025-06-0110311510.3390/recycling10030115An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping ApproachWeihan Sun0Quddus Tushar1Guomin Zhang2Andy Song3Lei Hou4Jingxuan Zhang5Shuxi Wang6School of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Computing Technologies, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Engineering, RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaConstruction and demolition waste (CDW) management remains a pressing challenge in the construction industry, contributing significantly to environmental degradation and resource depletion. Accurate waste measurement is essential for improving resource recovery and circular economy adoption. However, existing research lacks standardised estimation methods, the integration of digital technologies, and comprehensive lifecycle analysis approaches, limiting the effectiveness of waste prediction and management strategies. This study addresses the gap by conducting a scientometric analysis using CiteSpace and SciMAT, examining research trends, thematic clusters, and knowledge evolution in CDW quantification and management from 2014 to 2024. It establishes a conceptual framework for integrating digital systems and sustainable practices in CDW, focusing on waste generation rate, carbon emission, and phase-based waste management analysis. Network cluster analysis reveals the integral role of estimation tools and modelling techniques in refining waste generation quantification for building constructions. It also examines the interplay of digital tools, their influence on environmental cost reduction, and factors affecting waste production and environmental protection across project phases. This conjugate approach highlights the importance of the successful implementation of waste quantification and the imperative of machine learning for further investigation. This review offers an evidence-based framework to identify key stakeholders, guide future research, and implement sustainable waste management policies.https://www.mdpi.com/2313-4321/10/3/115science mappingconstruction and demolition waste (CDW)waste generation rate (WGR)building information modelling (BIM)artificial intelligence (AI)machine learning (ML) |
| spellingShingle | Weihan Sun Quddus Tushar Guomin Zhang Andy Song Lei Hou Jingxuan Zhang Shuxi Wang An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach Recycling science mapping construction and demolition waste (CDW) waste generation rate (WGR) building information modelling (BIM) artificial intelligence (AI) machine learning (ML) |
| title | An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach |
| title_full | An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach |
| title_fullStr | An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach |
| title_full_unstemmed | An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach |
| title_short | An Analytical Review of Construction and Demolition Waste Management and Quantification Methods Using a Science Mapping Approach |
| title_sort | analytical review of construction and demolition waste management and quantification methods using a science mapping approach |
| topic | science mapping construction and demolition waste (CDW) waste generation rate (WGR) building information modelling (BIM) artificial intelligence (AI) machine learning (ML) |
| url | https://www.mdpi.com/2313-4321/10/3/115 |
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