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Multisensor data fusion for the simultaneous location and condition assessment of underground water pipelines

Abdel-Aleem, Mostafa and Chibelushi, C.C. and Moniri, Mansour (2011) Multisensor data fusion for the simultaneous location and condition assessment of underground water pipelines. In: IEEE International Conference on Networking, Sensing and Control, 11-13 April 2011, Delft, Netherlands.

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Abstract or description

Underground water and waste-water pipelines are essential arteries and veins of developed cities and countries. Being hidden under the surface of the earth, these pipelines are at risk of being abandoned or damaged by “blind” construction machines digging the ground to place new utilities or undertake road or other construction works. Moreover, due to the high costs associated with the inspection and condition assessment of water pipelines, which can require the pipeline to be drained, water utility companies are often reluctant to inspect them. With the introduction of new and increasingly strict rules and regulations imposed by governments, the safety, security, structural integrity, and sustainability in operation of water pipelines are becoming strategic factors for utility companies in their quest to offer low failure probabilities under controlled and optimized costs. This paper, therefore, reviews the state-of-the-art techniques and sensor technologies currently used for either the location or the condition assessment of underground pipelines. It also introduces a new multisensor tool, typically employing ground penetrating radar and electromagnetic induction sensors, intended to simultaneously locate and assess the condition of underground pipelines from above the ground, hence minimizing the costs and reducing the problems associated with in-line inspections. Moreover, a novel multisensor data fusion system and architecture will also be introduced to fuse the information acquired from the utilized sensors. This novel architecture is designed within a heuristic framework, typically employing an artificial neural network at its heart.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
H100 General Engineering
Faculty: Previous Faculty of Computing, Engineering and Sciences > Computing
Depositing User: Claude CHIBELUSHI
Date Deposited: 12 May 2013 20:33
Last Modified: 01 Aug 2013 14:24
URI: http://eprints.staffs.ac.uk/id/eprint/1107

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