Journal
WASTE MANAGEMENT & RESEARCH
Volume 35, Issue 12, Pages 1285-1295Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0734242X17736381
Keywords
Building information modeling; demolition waste; estimation by type; framework
Categories
Funding
- National Research Foundation of Korea (NRF) grants - Korea government (MSIP) [NRF-2016R1A2A1A05005459, NRF-2017R1D1A1B03030660, NRF-2017R1D1A1B03033030]
- National Research Foundation of Korea [2017R1D1A1B03033030, 2017R1D1A1B03030660] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Most existing studies on demolition waste (DW) quantification do not have an official standard to estimate the amount and type of DW. Therefore, there are limitations in the existing literature for estimating DW with a consistent classification system. Building information modeling (BIM) is a technology that can generate and manage all the information required during the life cycle of a building, from design to demolition. Nevertheless, there has been a lack of research regarding its application to the demolition stage of a building. For an effective waste management plan, the estimation of the type and volume of DW should begin from the building design stage. However, the lack of tools hinders an early estimation. This study proposes a BIM-based framework that estimates DW in the early design stages, to achieve an effective and streamlined planning, processing, and management. Specifically, the input of construction materials in the Korean construction classification system and those in the BIM library were matched. Based on this matching integration, the estimates of DW by type were calculated by applying the weight/unit volume factors and the rates of DW volume change. To verify the framework, its operation was demonstrated by means of an actual BIM modeling and by comparing its results with those available in the literature. This study is expected to contribute not only to the estimation of DW at the building level, but also to the automated estimation of DW at the district level.
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