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An automated system for detection, classification and rehabilitation of defects in sewer pipes

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An automated system for detection, classification and rehabilitation of defects in sewer pipes

Shehab-Eldeen, Tariq (2001) An automated system for detection, classification and rehabilitation of defects in sewer pipes. PhD thesis, Concordia University.

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Abstract

The poor status of sewer pipes in North America has been reported by many researchers, revealing the presence of many defects that impact their performance. Inadequate inspection is considered as one of the main causes behind the declining condition of this class of pipes. This could be attributed to high cost of inspection and inadequate funds allocated to this purpose. The high cost is due to the current manual and high labor intensive inspection practice. Sewer rehabilitation methods are numerous and are constantly being developed. One of the rapidly expanding fields in the sewer rehabilitation industry is trenchless technology. Due to the large number of methods associated with this field, selecting the most suitable method manually can be a challenging task. Selection in this environment may also suffer from the limited knowledge and/or experience of the decision-maker. This research presents two developed automated systems: AUTO-DETECT and AUTO-SELECT. AUTO-DETECT detects and classifies defects in sewer pipes automatically. The system utilizes image analysis techniques, artificial intelligence (AI) and Visual Basic programming language for performing its task. A multiple classifier module encompassing a total of fifteen classifiers was developed to counter-check the results generated by the system. A solution strategy was also developed for efficient utilization of the developed specialized classifiers in an effort to improve the system's performance. The automated system was validated using actual data from randomly selected sections of the sewer network of a major Canadian municipality. The system's accuracy was found to range from 80% to 100%. AUTO-SELECT is essentially a multi-attribute decision support system designed to select and rank the most suitable trenchless rehabilitation methods for sewer pipes. The system utilizes two modules: (1) database management system (DBMS) and (2) decision support system (DSS). The developed relational database assists in identifying suitable trenchless rehabilitation techniques that satisfy a total of sixteen factors which account for technical, contractual and cost requirements of projects as well as user specified preferences. In case of having more than one suitable rehabilitation method, a DSS was developed to evaluate and rank them and, accordingly, suggest the most suitable one. A case example has been worked out to demonstrate the use and capabilities of the developed system.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Shehab-Eldeen, Tariq
Pagination:xxiii, 263 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (Ph.D.)
Program:Building, Civil and Environmental Engineering
Date:2001
Thesis Supervisor(s):Moselhi, Osama
ID Code:1624
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:20
Last Modified:08 Dec 2010 10:21
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