Technical Papers
Jul 10, 2023

An Integrated Assessment Framework of Economic, Environmental, and Human Health Impacts Using Scan-to-BIM and Life-Cycle Assessment in Existing Buildings

Publication: Journal of Management in Engineering
Volume 39, Issue 5

Abstract

The significance of environmental management of existing buildings in reducing the environmental impact of the construction sector is increasingly emphasized. However, life-cycle assessment (LCA) to evaluate a building’s environmental burden requires considerable time and costs for the collection and interpretation of data needed for analysis. Therefore, this study proposed an integrated assessment framework of economic, environmental, and human health impacts using scan-to-building information modeling (BIM) and LCA in existing buildings. To verify the proposed framework, a case study was conducted. A BIM model with an error rate of 1.442% can be automatically generated from the three-dimensional (3D) point cloud using parametric algorithm. The total cost for the target case was calculated at USD 162,769, and the environmental cost accounted for the largest percentage at 97.15%. The developed framework in this study holds significance because it enables the automated assessment of economic, environmental, and human health impacts of existing buildings through the integration of BIM and LCA.

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Data Availability Statement

Some of all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST, Ministry of Science, and ICT) (NRF-2021R1F1A1049692).

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Journal of Management in Engineering
Volume 39Issue 5September 2023

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Received: Mar 27, 2023
Accepted: Apr 25, 2023
Published online: Jul 10, 2023
Published in print: Sep 1, 2023
Discussion open until: Dec 10, 2023

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Graduate Research Assistant, Dept. of Architectural Engineering, Sejong Univ., 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea. Email: [email protected]
Hyeonggyun Kim [email protected]
Graduate Research Assistant, Dept. of Architectural Engineering, Sejong Univ., 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea. Email: [email protected]
Jaewook Lee [email protected]
Professor, Deep Learning Architecture Research Center, Dept. of Architectural Engineering, Sejong Univ., 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea. Email: [email protected]
Taehoon Hong, A.M.ASCE [email protected]
Underwood Distinguished Professor, Dept. of Architecture and Architectural Engineering, Yonsei Univ., Seoul 03722, South Korea. Email: [email protected]
Assistant Professor, Deep Learning Architecture Research Center, Dept. of Architectural Engineering, Sejong Univ., 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, South Korea (corresponding author). ORCID: https://orcid.org/0000-0001-9127-9243. Email: [email protected]

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