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Defect-oriented supportive bridge inspection system featuring building information modeling and augmented reality

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Abstract

Bridges are indispensable links of transportation infrastructure systems, and inspections play a critical role in maintaining bridge components in the state of good repair. Through a survey of bridge inspectors, the authors revealed that visual inspection techniques are the prominent inspection method but result in inaccuracy and ambiguity due to high variances among inspection results; modern inspections using drones and robots could improve efficiency but pose new challenges and do not reduce subjectivity. As a result, a novel, building information modeling- and augmented reality-based supportive inspection system (BASIS) that objectively captures bridge defects is proposed and validated. On-site inspectors can access the bridge model containing historical defect information (defect type, length/width/depth, and location) and overlay relevant content on the actual infrastructure through BASIS for inspection data collection with more accuracy and less ambiguity. A proof-of-concept prototype of the BASIS for bridges was developed as an android application and verified by bridge inspectors for effectiveness on a small pedestrian bridge. It was found that BASIS was able to collect accurate inspection data irrespective of the level of experience of the user, thusly minimizing the data subjectivity caused by differences among inspectors’ judgment and/or human errors. This research explores the utilization of emerging tools to collect bridge condition information in a more comprehensive and objective manner. Collected information can be further integrated it into a digital model that reflects the bridge’s most accurate and up-to-date condition, heading toward a digital twin of the physical infrastructure. The proposed system may also be adapted for other types of infrastructure (e.g., dams, levees, and railroads) that also require routine inspections.

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Author information

Authors and Affiliations

  1. Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, 22030, USA

    Immanuel John Samuel & Ossama Salem

  2. Department of Civil Engineering, College of Civil Engineering and Architecture, Wenzhou University, Zhejiang, 325035, China

    Song He

Authors
  1. Immanuel John Samuel
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  2. Ossama Salem
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  3. Song He
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Corresponding author

Correspondence to Song He.

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Appendix

Appendix

Appendix 1: Survey questions

The following questions are distributed online:

  1. 1.

    How long have you been in the profession of inspecting/managing bridges?

  2. 2.

    How difficult it is to record defect information (such as crack width, spall area, level of rust) and its location in traditional bridge inspection? (10 being extremely difficult)

  3. 3.

    How difficult it is to locate the defects recorded from the previous inspection on-site for further evaluation? (10 being extremely difficult)

  4. 4.

    Modern inspection techniques (such as drones, robotic vehicles) help to collect accurate data about defects and its location.

  5. 5.

    How difficult it is to operate the modern equipment (drones and robots) and/or to process the data collected? (10 being extremely difficult)

  6. 6.

    Which of the following issue(s) needs to be addressed for the adoption of modern inspection methods (such as drones and robotic vehicles) at a larger scale?

    1. i.

      Need of training/skilled personnel to operate these equipment

    2. ii.

      Greatly affected by environment condition (such as wind and rain)

    3. iii.

      Skeptical about the accuracy and data quality

    4. iv.

      Difficulty in managing huge volume of data over time

    5. v.

      Communication/coordination issue with post processing crew

  7. 7.

    Agree or disagree: finally, irrespective of the inspection method (traditional or modern), the condition rating is decided by the inspector.

Appendix 2: Survey responses

See Tables

Table 1 Responses claiming modern techniques improves accuracy
Full size table

1 and

Table 2 Responses claiming modern techniques does NOT improve accuracy
Full size table

2.

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John Samuel, I., Salem, O. & He, S. Defect-oriented supportive bridge inspection system featuring building information modeling and augmented reality. Innov. Infrastruct. Solut. 7, 247 (2022). https://doi.org/10.1007/s41062-022-00847-3

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