{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:55:21Z","timestamp":1777715721903,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2000,11,1]],"date-time":"2000-11-01T00:00:00Z","timestamp":973036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2000,11]]},"abstract":"<jats:p>Sewers are aging, expensive assets that attract public attention only when they fail. Sewer operators are under increasing pressure to minimise their maintenance costs, while preventing sewer failures. Inspection can give early warning of failures and allow economical repair under noncrisis conditions. Current inspection techniques are subjective and detect only gross defects reliably. They cannot provide the data needed to confidently plan long-term maintenance. This paper describes PIRAT, a quantitative technique for sewer inspection.PIRAT measures the internal geometry of the sewer and then analyses these data to detect, classify, and rate defects automatically using artificial intelligence techniques. We describe the measuring system and present and discuss geometry results for different types of sewers. The defect analysis techniques are outlined and a sample defect report presented. PIRAT\u2019s defect reports are compared with reports from the conventional technique and the discrepancies discussed. We relate PIRAT to other work in sewer robotics.<\/jats:p>","DOI":"10.1177\/02783640022067959","type":"journal-article","created":{"date-parts":[[2003,7,19]],"date-time":"2003-07-19T02:59:46Z","timestamp":1058583586000},"page":"1033-1053","source":"Crossref","is-referenced-by-count":81,"title":["PIRAT\u2014A System for Quantitative Sewer Pipe Assessment"],"prefix":"10.1177","volume":"19","author":[{"given":"Robin","family":"Kirkham","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick D.","family":"Kearney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin J.","family":"Rogers","sequence":"additional","affiliation":[{"name":"CSIRO Manufacturing Science and Technology, Locked Bag 9, Preston 3072 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Mashford","sequence":"additional","affiliation":[{"name":"CSIRO Building, Construction and Engineering, P.O. Box 56, Highett 3190 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2000,11,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"crossref","unstructured":"Broadhurst, S. J., Pridmore, T. P., and Taylor, N. 1994. Sensing for feature identification in sewers . Automation and Robotics in Construction 11: 675\u2013682 .","DOI":"10.1016\/B978-0-444-82044-0.50092-8"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1117\/12.950807"},{"key":"atypb3","unstructured":"City University, London . 1999. Thames Water\/OMC pipe profiling tool. Available: http:\/\/www.city.ac.uk\/omc\/profilin.htm."},{"key":"atypb4","unstructured":"Civil Engineering Research Foundation. 2000. Sewer scanner and evaluation technology (SSET). Available: http:\/\/www.cerf.org\/ceitec\/eval\/ongoing\/sset.htm."},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(97)00011-0"},{"key":"atypb6","unstructured":"Day, R. L. 1994. 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Development of a flexible utilisable robot for intelligent sensor-based sewer inspection . Proceedings of the 4th International Conference on Pipeline Construction, Hamburg, Germany."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1016\/S0886-7798(98)00026-1"},{"key":"atypb16","unstructured":"Mashford, J. S. 1995. A neural network image classification system for automatic inspection . Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"atypb17","doi-asserted-by":"crossref","unstructured":"McKim, R. A., and Sinha, S. K. 1999. Condition assessment of underground sewer pipes using a modified digital image processing paradigm . Trenchless Technology 14(2): 29\u201337 .","DOI":"10.1016\/S0886-7798(00)00021-3"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1016\/S0926-5805(99)00007-2"},{"key":"atypb19","unstructured":"Nakazato, T. 1997. Sewer optical fibre network in Tokyo. 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