Developing a methodological framework for adopting digitalization for deconstruction planning

Jin, R., Panuwatwanich, K., Adamu, Z., Madanayake, U. ORCID: https://orcid.org/0000-0002-9122-1882 and Ebohon, O. J. (2021) Developing a methodological framework for adopting digitalization for deconstruction planning. AIP Conference Proceedings, 2428 (1). 030001. ISSN 0094-243X doi: 10.1063/5.0071722 (International Conference On Construction Digitalisation For Sustainable Development: Transformation Through Innovation. 24–25 November 2020 Hanoi, Vietnam)

Abstract/Summary

Wastes generated from construction and demolition (C&D) activities account for a major proportion of urban solid wastes. The large amount of C&D waste generated are still being largely landfilled or downcycled worldwide. Aging built assets without proper C&D planning is a major cause of wastes not being efficiently diverted for circular use. How waste sorting could be properly planned in the early stages, not only for new construction, but also deconstruction projects is critical to minimizing C&D wastes. As there is a large amount of aging assets without digital files or even no drawings, developing a digital-driven approach to effectively estimate and categorize C&D wastes would be essential for, not just early-stage deconstruction planning, but also the development of a circular economy on C&D wastes. This study is first based on a thorough literature review of existing studies of applications of digital technologies to C&D waste diversion (WD). Limitations of existing studies are evaluated, such as the lack of digital twin approach for deconstruction. Then a methodological framework is established aiming to adopt digitalization for C&D WD, specifically for existing facilities under deconstruction planning. Based on the current work, future study would apply the methodological framework with real-world case studies to validate and test its effectiveness with initiated prototypes. Longer-term work can extend from the current framework to Internet-of-Things and Artificial Intelligence.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/116305
Identification Number/DOI	10.1063/5.0071722
Refereed	Yes
Divisions	No Reading authors. Back catalogue items Science > School of the Built Environment > Construction Management and Engineering
Publisher	AIP
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