Development of Methodology for Enhancing Visual Bridge Condition Assessment Using Image Processing Techniques

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Abstract:

Many bridge authorities have implemented Bridge Information Systems (BISs) or Bridge Management Systems (BMSs) to effectively manage their routine inspection information. The success of a BMS is highly dependent on the quality of bridge inspection outcomes and accurate estimation of future bridge condition ratings. To ensure such successful outcomes, a BMS must (1) contain reliable, consistent and accurate condition data from routine bridge inspections; and (2) encompass reliable deterioration modelling that overcomes the shortcomings of a lack of historical bridge inspection records. However published literature demonstrates that several limitations exist particularly in terms of inconsistency of inspection outcomes due to subjective judgment. To minimise such limitations, this paper presents a feasibility study for the enhancement of the current visual bridge inspection method using optical image processing techniques. The development work consists of image processing and knowledge-based approaches. It is anticipated that the proposed method is capable of minimising the shortcomings of subjective judgment on condition rating assessment and providing cost effective solutions to bridge agencies. Ultimately, the proposed bridge inspection methodology can provide consistent and accurate evaluation on the condition states of bridge elements. This in turn will lead to more reliable predictions of long-term bridge performance.

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1563-1570

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December 2012

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[1] M. Moore, B. Phares, B. Graybeal, D. Rolander and G. Washer: Reliability of visual inspection for highway bridges, Volume I: Final report and, Volume II: Appendices, U.S. Department of Transportation, Washington, D.C, FHWARD-01-020(021) (2001).

DOI: 10.3141/1749-14

Google Scholar

[2] B. Phares, G. Washer, D. Rolander, B. Graybeal and M. Moore: Routine highway bridge inspection condition documentation accuracy and reliability, Journal of Bridge Engineering, Vol. 9(4) (2004), p.403–413.

DOI: 10.1061/(asce)1084-0702(2004)9:4(403)

Google Scholar

[3] S. Chase and M. Edwards: Developing a Tele-Robotic Platform for Bridge Inspection, University of Virginia (2011).

Google Scholar

[4] K. L. Sanford, P. Herabat and S. Mcneil: Bridge Management and Inspection Data: Leveraging the Data and Identifying the Gaps, Transportation research board 8th International Bridge Management Conference, Denver, Colorado (1999).

Google Scholar

[5] R. S. Lim, H. M. La, Z. Shan and W. Sheng: Developing a crack inspection robot for bridge maintenance, IEEE conference publication (2011), pp.6288-6293

DOI: 10.1109/icra.2011.5980131

Google Scholar

[6] Z. Zhu, S. German, I and Brilakis: Detection of large-scale concrete columns for automated bridge inspection, Journal of Automation in Construction, Vol. 19 (8) (2010), pp.1047-1055.

DOI: 10.1016/j.autcon.2010.07.016

Google Scholar

[7] G. Sterritt: Review of Bridge Inspection Competence and Training, Project Report: Final, Research Project: UG637, ATKINS, (2009).

Google Scholar

[8] Queensland Department of Main Roads: Bridge Inspection Manual, Registration number 80.640, Queensland, Department of Main Roads, Transport Technology Division (2004).

Google Scholar

[9] Y. Fujita and Y. Hamamoto: A robust automatic crack detection method from noisy concrete surfaces, Machine Vision and Applications, Vol. 22 (2) (2011), pp.245-254.

DOI: 10.1007/s00138-009-0244-5

Google Scholar