Decision-Support System for the Rehabilitation of Deteriorating Sewers
Publication: Journal of Performance of Constructed Facilities
Volume 21, Issue 3
Abstract
This paper describes an automated and integrated detection, structural assessment, and rehabilitation method selection system for sewers based on the processing of video footage obtained by closed circuit television surveys. The system is based on a neural network classifier (NNC) trained to identify longitudinal cracks in sewers. Results obtained from experimentation with the NNC indicate that crack detection based on single-frame processing is not sufficient, and frame sequence processing substantially improves crack recognition rates. Based on the location of the cracks, local and global structural damage is assessed and a rehabilitation method is selected. Based on the significance of damaged sewers, the rehabilitation projects are being prioritized. An expert system coordinates the various modules in the system and connects them to a geographic information system.
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Acknowledgments
This research has been supported in part by EC DG XII for Science, Research, and Development in the Competitive and Sustainable Growth Program (RESEW), Contract No. G1RD-2001-00476). This publication reflects only the authors’ views. The European Community is not liable for any use that may be made of the information contained herein.
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 21 • Issue 3 • June 2007
Pages: 240 - 248
Copyright
© 2007 ASCE.
History
Received: Mar 25, 2005
Accepted: Jul 26, 2006
Published online: Jun 1, 2007
Published in print: Jun 2007
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