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Assessment and monitoring of bridges using various camera placements and structural analysis

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Abstract

This study presents a new vision-based deep learning method to monitor and evaluate the structural health of in-service infrastructure. For this purpose, three different camera placements, including remote, structure-mounted, and drone-mounted cameras, are proposed to capture the vibrations or displacements of bridges. The vision-based deep learning method is verified by an optical flow approach. Various techniques, such as visual data denoising and camera motion removal, are utilized to process the test data for displacement measurements and extract the structural frequencies. Structural models of bridges are analyzed to validate the measurements and assess the structural health of several pedestrian, traffic, and railway bridges without interfering with traffic. Measurements in the field experiments and results from the structural analysis on tested bridges show that the proposed framework works successfully and can be potentially engineered to monitor the structural health of existing bridges.

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Data availability

The data of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

In this study, Dr. Bing Zha and Jianli Wei helped us do experiments in the field. We appreciate their time and participation. The second author was supported by the 2219-International Postdoctoral Research Fellowship Program of The Scientific and Technological Research Council of Turkey (TUBITAK).

Funding

This study was funded by the National Science Foundation (grant number 2036193).

Author information

Author notes

  1. Yongsheng Bai, Aydin Demir, Alper Yilmaz and Halil Sezen have contributed equally to this work.

Authors and Affiliations

  1. Civil, Environmental and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, OH, 43210, USA

    Yongsheng Bai, Aydin Demir, Alper Yilmaz & Halil Sezen

  2. Neural Image Corporation, 1 Lake Bellevue, Bellevue, WA, 98005, USA

    Yongsheng Bai

  3. Department of Civil Engineering, Sakarya University, Universite Cd., 54050, Serdivan, Sakarya, Turkey

    Aydin Demir

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  1. Yongsheng Bai
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Correspondence to Halil Sezen.

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Bai, Y., Demir, A., Yilmaz, A. et al. Assessment and monitoring of bridges using various camera placements and structural analysis. J Civil Struct Health Monit 14, 321–337 (2024). https://doi.org/10.1007/s13349-023-00720-6

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  • DOI: https://doi.org/10.1007/s13349-023-00720-6

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