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Book Chapter

Automated Extraction of Bridge Gradient from Drawings Using Deep Learning

  • Hakan Bayer
  • Benedikt Faltin
  • Markus König

Digital methods such as Building Information Modeling (BIM) can be leveraged, to improve the efficiency of maintenance planning of bridges. However, this requires digital building models, which are rarely available. Consequently, these models must be created retrospectively, which is time-consuming when done manually. Naturally, there is a great interest in the industry to automate the process of retro-digitization. This paper contributes to these efforts by proposing a multistage pipeline to automatically extract the gradient of a bridge from pixel-based construction drawings using deep learning. The bridge gradient, a key element of the structure’s axis, is critical for describing the elevation profile and axis slope. This information is implicitly contained in the longitudinal view of bridge drawings as gradient symbols. To extract this information, the well-established object detection model YOLOv5 is employed to locate the gradient symbols inside the drawings. Subsequently, EasyOCR and heuristic rules are applied to extract the relevant gradient parameters associated with each detected symbol. The extracted parameters are then exported in a machine-interpretable format to facilitate seamless integration into other applications. The results show a promising 98% accuracy in symbol detection and an overall accuracy of 70%. Consequently, the pipeline represents a significant advance in automating the retro-digitization process for existing bridges by reducing the time and effort required

  • Keywords:
  • Building Information Modeling,
  • Computer Vision,
  • Deep Learning,
  • Symbol Detection,
  • Optical Character Recognition,
  • Construction Drawings,
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Hakan Bayer

Ruhr-University Bochum, Germany - ORCID: 0000-0002-8192-228X

Benedikt Faltin

Ruhr-University Bochum, Germany - ORCID: 0000-0003-1354-7817

Markus König

Ruhr-University Bochum, Germany - ORCID: 0000-0002-2729-7743

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  • Publication Year: 2023
  • Pages: 683-690

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  • Publication Year: 2023

Chapter Information

Chapter Title

Automated Extraction of Bridge Gradient from Drawings Using Deep Learning

Authors

Hakan Bayer, Benedikt Faltin, Markus König

DOI

10.36253/979-12-215-0289-3.68

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

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