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Decision-Support System for the Rehabilitation of Deteriorating Sewers |
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| Authors: | D Bairaktaris V Delis C Emmanouilidis S Frondistou-Yannas K Gratsias V Kallidromitis N Rerras |
| Affiliation: | 1Managing Director, DBA Ltd., 17 Pesmatzoglou St., Kifissia, Greece 14561. E-mail: baisteng@otenet.gr 2Project Manager, Computer Technology Institute, 61 Riga Feraiou St., Patras, Greece 26221. E-mail: delis@cti.gr 3Senior Researcher, CETI/ATHENA Research and Innovation Centre in Information Communication and Knowledge, 58 Tsimiski St., Xanthi, Greece 67100. E-mail: christosem@ieee.org 4Private Consultant, 46 Vasileos Constantinou Ave., Athens, Greece 11635. E-mail: matafron@otenet.gr 5Software Engineer, Computer Technology Institute, 61 Riga Feraiou St., Patras, Greece 26221. E-mail: gratsias@cti.gr 6Partner, TECNIC S.P.A., 86A Panama St., Rome, Italy 00198. 7Senior Software Engineer, ZENON SA Automation Technologies, 5 Kanari St., Glyka Nera, Athens, Greece 15354. E-mail: nrerras@zenon.gr |
| 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. |
| Keywords: | Neural networks Sewers Rehabilitation Inspection Deterioration |
