Abstracts – Browse Results
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Abu Dabous, S (2008) A decision support methodology for rehabilitation management of concrete bridges, Unpublished PhD Thesis, , Concordia University (Canada).
Al-Hussein, M (1999) An integrated information system for crane selection and utilisation, Unpublished PhD Thesis, , Concordia University (Canada).
Alshibani, A (2008) Optimizing and controlling earthmoving operations using spatial technologies, Unpublished PhD Thesis, , Concordia University (Canada).
Budawara, N (2009) Key performance indicators to measure design performance in construction, Unpublished PhD Thesis, , Concordia University (Canada).
El-Rayes, K A (1998) Optimized scheduling for repetitive construction projects, Unpublished PhD Thesis, , Concordia University (Canada).
Hammad, M M (2001) CPDICenter: Web-based virtual construction project document information center in support of claims preparation, Unpublished PhD Thesis, , Concordia University (Canada).
Hassanein, A (2003) Planning and scheduling highway construction using GIS and dynamic programming, Unpublished PhD Thesis, , Concordia University (Canada).
Hegazy, T M (1994) Integrated bid preparation with emphases on risk assessment using neural networks, Unpublished PhD Thesis, , Concordia University (Canada).
Iliescu, S (2000) A case-based reasoning approach to the designing of building envelopes, Unpublished PhD Thesis, , Concordia University (Canada).
Jrade, A (2004) Integrated conceptual cost estimating and life cycle costing system for building projects, Unpublished PhD Thesis, , Concordia University (Canada).
Li, J (2004) Web-based integrated project control, Unpublished PhD Thesis, , Concordia University (Canada).
Marzouk, M (2002) Optimizing earthmoving operations using computer simulation, Unpublished PhD Thesis, , Concordia University (Canada).
Meniru, K C U (2005) Computer-aided conceptual building design, Unpublished PhD Thesis, , Concordia University (Canada).
Morcous, G S L (2000) Case -based reasoning for modeling bridge deterioration, Unpublished PhD Thesis, , Concordia University (Canada).
Ravi, M (1998) Knowledge-based system approach to integrated design of multistorey office buildings at the preliminary stage, Unpublished PhD Thesis, , Concordia University (Canada).
Sadeghpour, F (2004) A CAD-based model for site layout, Unpublished PhD Thesis, , Concordia University (Canada).
Shehab-Eldeen, T (2002) An automated system for detection, classification and rehabilitation of defects in sewer pipes, Unpublished PhD Thesis, , Concordia University (Canada).
- Type: Thesis
- Keywords: accuracy; decision support; municipality; artificial intelligence; programming; rehabilitation
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/305476426
- 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.