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Journal of Civil Structural Health Monitoring

, Volume 6, Issue 4, pp 703–714 | Cite as

Defining a conceptual framework for the integration of modelling and advanced imaging for improving the reliability and efficiency of bridge assessments

  • Brodie Chan
  • Hong Guan
  • Lei Hou
  • Jun Jo
  • Michael Blumenstein
  • Jun Wang
Original Paper

Abstract

Current bridge inspection practices are typically predicated upon manual paper-based data collection methods, which significantly limit the ability to transfer knowledge gained throughout the lifecycle of the asset, to benefit the assessment of the inspector or engineer. This study aims to overcome the limitations of current practices and proposes a conceptual framework to improve the reliability and efficiency of current bridge asset management practices through the integration of Building Information Modeling (BIM) and advanced computing and imaging technologies. As a tool for bridge inspections, BIM offers significant potential when integrated with laser scanning and keypoint-based texture recognition, which allows for the detection of such defects as cracking, corrosion or settlement in bridge components. In recent years, the construction industry has seen an increased use of BIM technology on-site to aid the construction process. However, the applications of it are deficient through the asset management phases of a project. Given the ability of BIM to house all component specific information gathered from the construction, inspection and maintenance phases, BIM is envisioned to allow emphasis to be placed on retrieving the relevant information throughout the project lifecycle, ultimately enabling engineers and bridge inspectors to make more informed decisions about the current condition of the structure. Using BIM as the focal point for information collection throughout the project lifecycle, findings from advanced imaging and data processing are proposed to be stored within the model for recall at future bridge assessments.

Keywords

BIM Keypoint-based texture recognition Laser scanning Bridge inspection Condition assessment Bridge asset management 

Notes

Acknowledgments

The authors wish to thank Shanghai Investigation Design & Research Institute Co., Ltd in China for providing the case study of the Yongxin Floodgate Project. The authors would like to additionally acknowledge the information provided by the Sydney Harbour Foreshore Authority for the Pyrmont Bridge BrIM project.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Brodie Chan
    • 1
    • 2
  • Hong Guan
    • 1
  • Lei Hou
    • 1
  • Jun Jo
    • 3
  • Michael Blumenstein
    • 4
  • Jun Wang
    • 5
  1. 1.Griffith School of EngineeringGriffith UniversityGold CoastAustralia
  2. 2.GHD Pty LtdBrisbaneAustralia
  3. 3.School of Information and Communication TechnologyGriffith UniversityGold CoastAustralia
  4. 4.School of SoftwareUniversity of Technology SydneySydneyAustralia
  5. 5.Australasian Joint Research Centre for Building Information ModellingCurtin UniversityPerthAustralia

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