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78 Reliability and Maintainability of In-Service Pipelines

Ritter, M. and Frey. C.W. (2010) ‘Rotating optical geometry sensor for inner pipe-surface reconstruc-
tion.’ Proc. SPIE 7538, Image Processing: Machine Vision Applications III, San Jose, California,
January 17, 2010. 7538. 03.
Rogers, C.D.F., Hao, T., Burrow, M.P.N., Costello, S.B., et al., 2012. Condition assessment of the sur-
face and buried infrastructure - a proposal for integration. Tunnel. Underground Space Technol.
28, 202 211.
Sarshar, N., Halfawy, M.R., Hengmeechai, J., 2009. Video processing techniques for assisted CCTV
inspection and condition rating of sewers. J. Water Manage. Model NRCC-50451.
Stani´ c, N., VanDerSchoot, W.P.J., Kuijer, B., Langeveld, J.G., Clemens, F.H.L.R. (2013) ‘Potential of
laser scanning for assessing structural condition and physical roughness of concrete sewer pipes.’ 7th
International Conference on Sewer Processes & Networks, SPN7, August 28 30,Sheffield,UK.

Further Reading

Hong, H.P., 1999. Inspection and maintenance planning of pipeline under external corrosion consider-
ing generation of new defects. Struct. Saf. 21, 203 222.
Isa, D., Rajkumar, R., 2009. Pipeline defect prediction using support vector machine. Appl. Artif.
Intell. 23 (8), 758 771.
Khodayari-Rostamabad, A., Reilly, J.P., Nikolova, N.K., Hare, J.R., Pasha, S., 2009. Machine learning
techniques for the analysis of magnetic flux leakage images in pipeline inspection. Electr.
Comput. Eng. Dep. 45 (8), 3073 3084.