Automated prediction of condition state rating in bridge inspection

2012 Proceedings of the 29th International Symposium of Automation and Robotics in Construction, ISARC 2012

Volumn , Issue , 2012, Pages

  Adhikari, R S ^a   Moselhi, O ^a   Bagchi, A ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

Bridge inspection; Condition state rating; Image analysis; Neural networks

Indexed keywords

CONCRETES; DIGITAL CAMERAS; FORECASTING; IMAGE ANALYSIS; INSPECTION; NEURAL NETWORKS; PHOTOGRAPHIC ACCESSORIES; ROBOTICS; SURFACE DEFECTS; VIDEO CAMERAS;

BACK PROPAGATION NEURAL NETWORKS; BRIDGE INSPECTION; CONDITION STATE; CORRELATION COEFFICIENT; IMAGE PROCESSING TECHNIQUE; MAINTENANCE AND REHABILITATIONS; RED , GREEN AND BLUES; RISK BASED INSPECTION;

IMAGE PROCESSING;

EID: 85088186550     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.4017/gt.2012.11.02.153.00     Document Type: Conference Paper

References (23)

1. Bisby, L.A. and Briglio, M.B., ISIS Canada Educational Module No. 5: An Introduction to Structural Health Monitoring, Page 3, 2004.
2. Golabi, K., Thompson, P., Hyman, W., Pontis: a network optimization system for bridge improvements and maintenance Technical manual. FHWA-SA-94-031, 1993.
3. Abudayyeh, O., Al Bataineh, M., Abdel-Qader, I., "An imaging data model for concrete bridge inspection,", Advances in Engineering Software, Vol. 35(8-9), pp. 473-480, 2004.
4. Abudayyeh, O., Al-Battaineh, H. T., "As-built information model for bridge maintenance,", Journal of Computing in Civil Eng., Vol. 17(2), pp. 105-112, 2003.
5. Gole, B., Management Vs Crisis reaction. Report. Chicago, Il, American public works association, 1985.
6. Jivacate, I., Najafi, F.T., The current status of bridge management systems, Canadian society of civil engineering, pp. 1622-1631, 2003.
7. Federal Highway Administration (FHWA) 2002, Bridge Inspector's manual reference, FHWA NHI 03-001, 2002.
8. Al-Wazeer, A., Ayyub, B.M., Risk-based bridge maintenance strategies, PhD thesis, 2007.
9. White paper on bridge inspection and rating, Journal of Bridge Engineering, Vol. 14(1), pp. 1-5, 2009.
10. American society of civil engineers, NTSB Expected to adopt final report on I-35 W Bridge Collapse, 2008.
11. Hachem, Y., Bridge Inspection Strategies, PhD thesis, 1990.
12. Wirsching, P.H., Yi, T., "Optimal Strategies for Design, Inspection, and Repair of Fatigue-Sensetive Structural Systems Using Risk-Based Economics,", Structural Safety Rehabilitation, pp. 2107-2114, 1989.
13. Liu, M., Frangopol, D., Decision support system for bridge network maintenance planning, 2006.
14. Liu, W., Terrestrial LiDAR-based bridge evaluation, PhD thesis, 2010.
15. Bagchi, A., Humar, J. Noman, A., "Development of a Finite Element System for Vibration Based Damage Identification in Structures,", J of Applied Sciences, Vol. 7(17), pp. 2404-2413, 2007.
16. Fryer, J.G., Close range photogrammetry meets machine vision, Journal of photogrammetry and remote sensing, Vol. 46(3), pp. 173, 1991.
17. Jauregui, D.V., Tian, Y., Ruinian, J., Photogram-metry application in routine Bridge Inspection and Historic Bridge Documentation, 2006.
18. Moselhi, O., Shehab, T., "Classification of defects in sewer pipes using neural networks,", Journal of Infrastructure System, Vol. 6(3), 97-104, 2000.
19. Khan, Z., et al., "Structural condition assessment of sewer pipeslines,", Journal of performance of constructed facilities, Vol. 24(2), pp. 170-179, 2010.
20. Office of the Auditor General of Ontario, Auditor General releases his 2011 Annual Report, 2011.
21. Neuroshell 2, Online Documentation, http://www.wardsystems.com/manuals/ neuroshell2/index.html?idxhowuse.htm
22. ImageJ: Comercial software for image analysis, Online Resources, http://rsb.info.nih.gov/ij/
23. Ontario Structure Insoection Manual (OSIM), http://www.ogra.org/lib/ db2file.asp?fileid=24271