Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/173344
Title: As-built BIM reconstruction of piping systems using smartphone videogrammetry and terrestrial laser scanning
Authors: Xie, Yuan
Teo, Mou Xuan
Li, Siyi
Huang, Lihui
Liang, Nanying
Cai, Yiyu
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Xie, Y., Teo, M. X., Li, S., Huang, L., Liang, N. & Cai, Y. (2023). As-built BIM reconstruction of piping systems using smartphone videogrammetry and terrestrial laser scanning. Automation in Construction, 156, 105120-. https://dx.doi.org/10.1016/j.autcon.2023.105120
Journal: Automation in Construction
Abstract: The increasing adoption of Building Information Modeling (BIM) in the construction industry has created a demand for methods to update existing facilities with accurate as-built BIM models. While terrestrial laser scanners (TLS) are commonly used for indoor surveys due to their speed and precision, they face limitations in areas with physical constraints like confined spaces and occlusions, with pipes being particularly affected. This paper presents a smartphone videogrammetry approach designed to enhance the accuracy and completeness of terrestrial laser scanner (TLS) point clouds for as-built BIM reconstruction. The method involves capturing missing areas within the TLS point cloud with smartphones and then seamlessly integrating these data into a more comprehensive multi-modal point cloud. This approach is validated using real scan data and offers convenience through the use of readily available and cost-effective smartphones, while its iterative nature facilitates progressive BIM reconstruction of pipe structures. Additionally, a compatible progressive BIM reconstruction framework is outlined, aiming to modernize scan-to-BIM practices in the construction industry, with a case study demonstrating its feasibility.
URI: https://hdl.handle.net/10356/173344
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2023.105120
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Surbana Jurong-NTU Corporate Lab
Rights: © 2023 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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