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Book Chapter

As-Built Detection of Structures by the Segmentation of Three-Dimensional Models and Point Cloud Data

  • Nobuyoshi Yabuki
  • Tomohiro Fukuda
  • Ryu Izutsu

At construction sites, as-built management is generally conducted by taking pictures or surveying with total stations and comparing the images or survey data with design drawings or Building Information Modeling (BIM) models. Since this work is time-consuming and error-prone, more efficient and accurate methods using advanced Information and Communication Technology (ICT) are desired. Therefore, this research proposes a method that can efficiently capture the progress of construction by detecting each constructed structural member, such as beams, columns, connections, etc. In this proposed method, construction engineers first take many pictures of the construction site and conduct automatic image segmentation using a pre-trained Convolutional Neural Network (CNN) model. Next, point cloud data is generated from taken pictures by using Structure from Motion (SfM). Then, the point cloud data is semantically segmented by overlapping the segmented images and point cloud data using the pin-hole camera technique. Finally, the design BIM model and segmented point cloud data are overlapped, and constructed parts of the BIM model can be detected, which can be reported as as-built parts. A prototype system was developed and applied to an actual railway construction project in Osaka, Japan for testing the accuracy and performance of the system

  • Keywords:
  • Construction progress management,
  • Instance segmentation,
  • Point cloud,
  • Building Information Modeling.,
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Nobuyoshi Yabuki

Osaka University, Japan - ORCID: 0000-0002-2944-4540

Tomohiro Fukuda

Osaka University, Japan - ORCID: 0000-0002-4271-4445

Ryu Izutsu

Kajima Corporation, Japan

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  • Publication Year: 2023
  • Pages: 1117-1124

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  • Publication Year: 2023

Chapter Information

Chapter Title

As-Built Detection of Structures by the Segmentation of Three-Dimensional Models and Point Cloud Data

Authors

Nobuyoshi Yabuki, Tomohiro Fukuda, Ryu Izutsu

DOI

10.36253/979-12-215-0289-3.111

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

52

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