Daher, Sami (2015) Defect-based Condition Assessment Model and Protocol of Sewer Pipelines. Masters thesis, Concordia University.
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Abstract
Infrastructure serves as the backbone of the city and hence plays a significant role in its urban structure. Therefore, it is of utmost importance to monitor its performance and assure its compliance with the growth in demand. Due to their hidden and passive nature, sewer pipelines are neglected making it essential to assess their conditions and address their associated problems to maintain quality productivity and avoid high social costs. Currently, 30% of the Canadian Infrastructure has been evaluated to be in fair to very poor conditions with a cost of $39 billion for infrastructure repair (Felio et al. 2012).In 2008, it was stated that the capital investment needs in the United States are $15 billion annually for the coming 20 years totaling to $298 billion. Moreover, the pipelines in the U.S represent 3/4th of the total needs marking the largest capital need (ASCE 2013). The current condition assessment protocols are limited to several issues including poor accuracy caused by uncertain human judgments and imprecise assessments due to the consideration of the peak score (worst defect) as the total condition score. Therefore, the development of a sound condition assessment protocol with a unified classification of distress indicators regardless of the inspector’s expertise is needed to ensure safety and quality service to the public.
The objective of this research is to develop a defect-based condition assessment model as well as a protocol for sewer pipelines. This model aims to cover the structural, operational, and installation / rehabilitation defects that are associated with the pipelines, joints, and manholes of each pipe length / segment. This Fuzzy Synthetic Evaluation model consists of the Analytic Network Process (ANP) model which covers the interdependencies between the components and their defects in order to deduce their relative importance weights. The second model utilizes the defects’ severities to develop fuzzy membership functions based on a predefined linguistic condition grading scale that would precisely indicate the degree of distress. This model quantifies the distress indicators and encodes their condition linguistically (states) and numerically (scores). Furthermore, a robust aggregation model based on the Hierarchical Evidential Reasoning (HER) and Dempster-Shafer (D-S) theory is created to integrate the defects’ conditions and to evaluate the overall condition of the sewer pipeline. Also, the main grading scale in this model was developed using the K-Means clustering technique. The final condition grade is represented as a crisp value calculated by the weighted average defuzzification method. The data utilized in this research was obtained from sewer condition classification manuals, previous research, and questionnaires distributed to professionals in Qatar and Canada. Also, a sewer protocol was developed, calibrated, and verified by experts’ feedback. The fruit of this fusion was also presented in a user-friendly automated tool (SPCAT). The developed model was implemented in 29 case studies from Montreal and Qatar. The predicted results of 15 inspected pipelines in Montreal, Canada, resulted in mean absolute error values for structural and operational defects of 0.533 and 0.267 respectively with correlation coefficients of 0.846 and 0.934. The second batch of 14 inspected pipe segments in Qatar, resulted in a mean absolute error of 0.643 and a correlation coefficient of 0.60 between the predicted and real values .The results are justified throughout the research body.This model helps in minimizing the inaccuracy of sewer condition assessment through the application of severity, uncertainty mitigation, and robust aggregation. It also benefits asset managers by providing a precise condition overview for maintenance, rehabilitation, and budget allocation purposes.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Daher, Sami |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Building Engineering |
| Date: | 15 July 2015 |
| Thesis Supervisor(s): | Zayed, Dr. Tarek |
| Keywords: | Defect Based Sewer Pipeline Condition Assessment and Protocol |
| ID Code: | 980210 |
| Deposited By: | SAMI DAHER |
| Deposited On: | 05 Jul 2016 13:54 |
| Last Modified: | 18 Jan 2018 17:50 |
References:
Ahmadi, M., Cherqui, F., de Massiac, J., Werey, C., Lagoutte, S., and Le Gauffre, P. (2014)."Condition Grading for Dysfunction Indicators in Sewer Asset Management." Structure and Infrastructure Engineering, 10(3), 346-358.Al-Barqawi, H., and Zayed, T. (2008). "Infrastructure Management: Integrated AHP/ANN Model to Evaluate Municipal Water Mains’ Performance." Journal of Infrastructure Systems, 14(4), 305-318.
Al-Barqawi, H., and Zayed, T. (2006). "Condition Rating Model for Underground Infrastructure Sustainable Water Mains." Journal of Performance of Constructed Facilities, 20(2), 126-135.
Allouche, E. N., and Freure, P. (2002). Management and Maintenance Practices of Storm and Sanitary Sewers in Canadian Municipalities. Institute for Catastrophic Loss Reduction.
American Society of Civil Engineers, American Society of Civil Engineers, American Society of Civil Engineers, Water Environment Federation, and Water Environment Federation. (1994). Existing Sewer Evaluation and Rehabilitation. The Federation, Alexandria, VA.
Ana, E. (2009). "Sewer Asset Management-Sewer Structural Deterioration Modeling and Multicriteria Decision Making in Sewer Rehabilitation Projects Prioritization." Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels.
ASCE. (2013). "Report Card for America's Infrastructure." American Society of Civil Engineers, http://www.infrastructurereportcard.org.
Bai, H., Sadiq, R., Najjaran, H., and Rajani, B. (2008). "Condition Assessment of Buried Pipes Using Hierarchical Evidential Reasoning Model." Journal of Computing in Civil Engineering, 22(2), 114-122.
Baur, R., and Herz, R. (2002). "Selective Inspection Planning With Ageing Forecast for Sewer Types." Water Science & Technology, 46(6), 389-396.
Boshoff, S., Childs, R., and Roberts, L. (2009). "Guidelines for Infrastructure Asset Management in Local Government 2006 – 2009." Department of Provincial and Local Government, REPUBLIC OF SOUTH AFRICA, Pretoria.
Büyükyazıcı, M., and Sucu, M. (2003). "The Analytic Hierarchy and Analytic Network Processes." Hacettepe Journal of Mathematics and Statistics, 32, 65-73.
CEN (European Committee for Standardization). (2003). EN 13508-2: 2003, Conditions of Drain and Sewer Systems Outside Buildings, Part 2: Visual Inspection Coding System. CEN, Berlin, Germany.
Centre for Expertise and Research on Infrastructures in Urban Areas (CERIU). (2004). Manuel de Standardization des Observations. CERIU, Montreal, Quebec, Canada.
Chae, M., and Abraham, D. (2001). “Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment.” Journal of Computing in Civil Engineering, 15(1), 4-14.
Chughtai, F., and Zayed, T. (2011). "Integrating WRc and CERIU Condition Assessment Models and Classification Protocols for Sewer Pipelines." Journal of Infrastructure Systems, 17(3), 129-136.
Chughtai, F., and Zayed, T. (2008). "Clustered Models for the Integration of Sewer Condition Classification Protocols." Pipelines 2008’s Pipeline Asset Management: Maximizing Performance of our Pipeline Infrastructure, ASCE, 1-11.
Chughtai, F., and Zayed, T. (2008). "Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines." Journal of Perforance of Constructed Facilities, 22(5), 333-341.
Chughtai, F., and Zayed, T. (2007a). "Sewer Pipeline Operational Condition Prediction Using Multiple Regression." Proceedings, Pipelines 2007: Advances and Experiences with Trenchless Pipeline Projects, ASCE, Boston, July 8–11.
Chughtai, F., and Zayed, T. (2007b). "Structural Condition Models for Sewer Pipeline." Proceedings, Pipelines 2007: Advances and Experiences with Trenchless Pipeline Projects, ASCE, Boston, July 8–11.
Civil Engineering Research Foundation. (2001). Evaluation of SSET: The Sewer Scanner & Evaluation Technology. American Society of Civil Engineers, Reston, VA.
Daniels, D. J. (2005). Ground Penetrating Radar. Wiley Online Library.
El-Abbasy, M. S., Senouci, A., Zayed, T., Mirahadi, F., and Parvizsedghy, L. (2014). "Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis." Journal of Construction Engineering and Management, 140(6).
El-Abbasy, M. S., Senouci, A., Zayed, T., and Mosleh, F. (2015). "A Condition Assessment Model For Oil And Gas Pipelines Using Integrated Simulation And Analytic Network Process." Structure and Infrastructure Engineering, 11(3), 263-281.
Elbeltagi, E. E., Hosny, O. A., Elhakeem, A., Abdelrazek, M. E., and El-Abbasy, M. S. (2012). "Fuzzy Logic Model for Selection of Vertical Formwork Systems." Journal of Construction Engineering and Management, 138(7), 832-840.
Ennaouri, I., and Fuamba, M. (2011). "New Integrated Condition-Assessment Model for Combined Storm-Sewer Systems." Journal of Water Resources Planning and Management, 139(1), 53-64.
Feeney, C. S. (2009). White Paper on Condition Assessment of Wastewater Collection Systems. National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency.
Felio, G., Canadian Construction Association, and Canadian Public Works Association. (2012). Canadian Infrastructure Report Card: Volume 1: 2012 Municipal Roads and Water Systems. Canadian Construction Association.
Figueira, J., Greco, S., and Ehrgott, M. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. Springer Science & Business Media.
Gencer, C., and Gürpinar, D. (2007). "Analytic Network Process in Supplier Selection: A Case Study in an Electronic Firm." Applied Mathematical Modelling, 31(11), 2475-2486.
Gordon, J., and Shortliffe, E. H. (2008). "A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space." Classic Works of the Dempster-Shafer Theory of Belief Functions, Springer, 311-344.
Gordon, J., and Shortliffe, E. H. (1985). "A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space." Artificiel Intelligence, 26(3), 323-357.
Grondin, B. (2012). Guide Pour Comprendre Et Interpréter Le Protocole D'inspection Télévisée PACP. CERIU, Montreal, Quebec.
Hao, T., Rogers, C., Metje, N., Chapman, D., Muggleton, J., Foo, K., Wang, P., Pennock, S., Atkins, P., and Swingler, S. (2012). "Condition Assessment of the Buried Utility Service Infrastructure." Tunnelling and Underground Space Technology, 28, 331-344.
Kabir, G., Sadiq, R., and Tesfamariam, S. (2014). "A Review of Multi-Criteria Decision-Making Methods for Infrastructure Management." Structure and Infrastructure Engineering, 10(9), 1176-1210.
Kathula, V. S. (2001). "Structural Distress Condition Modeling For Sanitary Sewers". Doctor of Philosophy. Louisiana Tech University, Louisiana, United States.
Khan, Z., Zayed, T., and Moselhi, O. (2009). "Structural Condition Assessment of Sewer Pipelines." Journal of Performance of Constructed Facilities, 24(2), 170-179.
Khazraeializadeh, S. (2012). A Comparative Analysis on Sewer Structural Condition Grading Systems using Four Sewer Condition Assessment Protocol. Edmonton: Faculty of Graduate Studies and Research- The University of Alberta.
Koo, D., and Ariaratnam, S. T. (2006). "Innovative Method for Assessment of Underground Sewer Pipe Condition." Automation in Construction, 15(4), 479-488.
Le Gauffre, P., Joannis, C., Vasconcelos, E., Breysse, D., Gibello, C., and Desmulliez, J. (2007). "Performance Indicators and Multicriteria Decision Support For Sewer Asset Management." Journal of Infrastructure Systems, 13(2), 105-114.
Meade, L. M., and Presley, A. (2002). "R&D Project Selection Using the Analytic Network Process." Engineering Management, IEEE Transactions on, 49(1), 59-66.
Moselhi, O., and Shehab-Eldeen, T. (2000). "Classification of Defects In Sewer Pipes Using Neural Networks." Journal of Infrastructure Systems, 6(3), 97-104.
Najafi, M., & Kulandaivel, G. (2005). Pipeline Condition Prediction Using Neural Network Models. Pipeline Division Specialty Conference, 21-24 August 2005 (pp. 767-781). Houston, Texas, United States: American Society of Civil Engineers.
New Zealand Water and Wastes Association Inc. (2006). New Zealand Pipe Inspection Manual. New Zealand Water and Wastes Association Inc, New Zealand.
Opila, M. C., and Attoh-Okine, N. (2011). "Novel Approach in Pipe Condition Scoring." Journal of Pipeline Systems Engineering and Practice, 2(3), 82-90.
Rahman, S., and Vanier, D. (2004). "An Evaluation of Condition Assessment Protocols for Sewer Management." NRC, Canada.
Rajani, B., Kleiner, Y., and Sadiq, R. (2006). "Translation of Pipe Inspection Results into Condition Ratings Using the Fuzzy Synthetic Evaluation Technique." Aqua- Journal of Water Supply: Research and Technology, 55(1), 11-24.
Ross, T. J. (2010). Fuzzy logic with engineering applications (3rd Edition). New Mexico, USA: John Wiley & Sons, Ltd. John Wiley & Sons.
Rowe, R., Kathula, V., Bergin, J., and Kennedy, C. C. (2011). "Asset Management Likelihood of Failure Scoring Improved by Condition Assessment Scoring Integration Techniques." Reston, VA: ASCE copyright Proceedings Of The Pipelines 2011 Conference, July 23-27, 2011, Seattle, Washington| d 20110000, American Society of Civil Engineers, .
Saaty, T. L. (2001). Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity. RWS Publications, Pittsburgh, PA.
Saaty, T. L., Vargas, L. G., and SpringerLink. (2012). Models, Methods, Concepts & Applications of The Analytic Hierarchy Process. Springer, New York.
Saaty, T. L., Vargas, L. G., and SpringerLink. (2006). Decision Making With The Analytic Network Process. Springer, New York.
Saha, S., Mukhopadhyay, S., Mahapatra, U., Bhattacharya, S., and Srivastava, G. (2010). "Empirical Structure for Characterizing Metal Loss Defects From Radial Magnetic Flux Leakage Signal." NDT E Int., 43(6), 507-512.
Sentz, K., and Ferson, S. (2002). Combination of Evidence in Dempster-Shafer Theory. Citeseer, Albuquerque, New Mexico: Sandia National Laboratories.
Sönmez, M., Holt, G., Yang, J., and Graham, G. (2002). "Applying Evidential Reasoning to Prequalifying Construction Contractors." Journal of Management in Engineering, 18(3), 111-119.
Tagherouit, W. B., Bennis, S., and Bengassem, J. (2011). "A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks." Computer‐Aided Civil and Infrastructure Engineering, 26(2), 146-152.
Thornhill, R. (2008). "Know Your Limitations with PACP Condition Grading." (Summer 2008), 12.
Thornhill, R., and Wildbore, P. (2005). "Sewer defect codes: Origin and destination." U-Tech Underground Construction Paper.
Van Leekwijck, W., and Kerre, E. E. (1999). "Defuzzification: Criteria and Classification." Fuzzy Sets and Systems, 108(2), 159-178.
Vanier, D., and Rahman, S. (2004). "MIIP Report: A Primer on Municipal Infrastructure Asset Management." Institute for Research in Construction, National Research Council Canada, Ottawa.
Wang, J., Yang, J., and Sen, P. (1995). "Safety Analysis and Synthesis Using Fuzzy Sets and Evidential Reasoning." Reliability Engineering and System Safety, 47(2), 103-118.
WRc. (2013). Manual of Sewer Condition Classification.Fifth Edition, WRc, plc, UK.
WRc. (2001). Sewerage Rehabilitation Manual, Fourth Edition, Water Research Centre, UK.
WRc plc. (2015). "Sewerage Risk Management (SRM)." http://srm.wrcplc.co.uk/ (04/16, 2014).
Yager, R. R., and Liu, L. (2008). Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer Science & Business Media.
Yan, J., and Vairavamoorthy, K. 2003. “Fuzzy approach for pipe condition assessment.” Proc., New Pipeline Technologies, Security, and Safety, ASCE, Reston, Va., 466–476.
Yang, C., Chuang, S., Huang, R., and Tai, C. (2008). "Location selection based on AHP/ANP approach." Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on, IEEE, 1148-1153.
Yang, J., and Singh Madan, G. (1994). "An Evidential Reasoning Approach for Multiple-Attribute Decision Making With Uncertainty." Systems, Man and Cybernetics, IEEE Transactions on, 24(1), 1-18.
Yang, J., Wang, Y., Xu, D., and Chin, K. (2006). "The Evidential Reasoning Approach for MADA under both Probabilistic and Fuzzy Uncertainties." European Journal of Operational Research, 171(1), 309-343.
Yang, J., and Xu, D. (2002). "On The Evidential Reasoning Algorithm for Multiple Attribute Decision Analysis under Uncertainty." Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 32(3), 289-304.
Zhang, C., and Xia, S. (2009). "K-Means Clustering Algorithm with Improved Initial Center." Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on, IEEE, 790-792.
Zhao, J. Q. (1998). "Trunk Sewers in Canada." APWA International Public Works Congress Seminar Series, American Public Works Association, Las Vegas, 75-89.
Zhao, J. Q., McDonald, S. E., and Kleiner, Y. (2001). "Guidelines for Condition Assessment and Rehabilitation of Large Sewers." Institute for Research in Construction, National Research Council Canada, Ottawa.
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