Quality Function Deployment Application in Sewer Pipeline Assessment
Authors: Khalid Kaddoura, Tarek Zayed
Abstract:
Infrastructure assets are essential in urban cities; their purpose is to facilitate the public needs. As a result, their conditions and states shall always be monitored to avoid any sudden malfunction. Sewer systems, one of the assets, are an essential part of the underground infrastructure as they transfer sewer medium to designated areas. However, their conditions are subject to deterioration due to ageing. Therefore, it is of great significance to assess the conditions of pipelines to avoid sudden collapses. Current practices of sewer pipeline assessment rely on industrial protocols that consider distinct defects and grades to conclude the limited average or peak score of the assessed assets. This research aims to enhance the evaluation by integrating the Quality Function Deployment (QFD) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods in assessing the condition of sewer pipelines. The methodology shall study the cause and effect relationship of the systems’ defects to deduce the relative influence weights of each defect. Subsequently, the overall grade is calculated by aggregating the WHAT’s and HOW’s of the House of Quality (HOQ) using the computed relative weights. Thus, this study shall enhance the evaluation of the assets to conclude informative rehabilitation and maintenance plans for decision makers.
Keywords: Condition assessment, DEMATEL, QFD, sewer pipelines.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316005
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 830References:
[1] K. Kaddoura, Automated Sewer Inspection Analysis and Condition Assessment. Montreal, QC, 2015.
[2] R. Wirahadikusumah, D. Abraham, T. Iseley, and R. K. Prasanth, “Assessment technologies for sewer system rehabilitation,” Automation in Construction, vol. 7, 1998, pp. 259 -270.
[3] R. Kirkham, P. D. Kearney, K. J. Rogers, and J. Mashford, “PIRAT—a system for quantitative sewer pipe assessment,” The International Journal of Robotics Research, vol. 19, 2000, pp. 1033 – 1053.
[4] Y. Kleiner, “Scheduling inspection and renewal of large infrastructure assets,” Journal of Infrastructure Systems, vol. 7, 2001, pp. 136 – 143.
[5] Canadian Infrastructure, Informing the Future. Canada, 2016.
[6] S. Daher, Defect-based Condition Assessment Model and Protocol of Sewer Pipelines. Montreal, QC, 2015.
[7] T. Angkasuwansiri, and S. K. Sinha, “Development of a robust wastewater pipe performance index,” Journal pf Performance of Constructed Facilities, 2014.
[8] K. Kaddoura, T. Zayed, and A. Hawari, “Multi-Attribute Utility Theory Deployment in Sewer Defects Assessment,” Journal of Computing in Civil Engineering, accepted for publication.
[9] G. Kulandaivel, Sewer Pipeline Condition Prediction Using Neural Network Models. Michigan, USA, 2004.
[10] M. Najafi, and G. Kulandaivel, “Pipeline condition prediction using neural network models,” Pipelines 2005: Optimizing Pipeline Design, Operations, and Maintenance in Today’s Economy, 2005, pp. 767-781.
[11] J. Mashford, D. Marlow, D. Tran, and R. May, “Prediction of sewer condition grade using support vector machines,” Journal of Computing in Civil Engineering, vol. 25, 2010, pp. 283-290.
[12] J. Ruwanpura, S. Ariratnam, A. El-Assaly, “Prediction models for sewer infrastructure utilizing rule-based simulation,” Civil Engineering and Environmental Systems, vol. 21, 2004, pp. 169-185.
[13] F. Chugjtai, and T. Zayed, “Infrastructure condition predication model for sustainable sewer pipelines,” Jounral of Performance of Constructed Facilities, vol. 22, 2008, pp. 333-341.
[14] I. Bakry, H. Alzraiee, M. Masry, K. Kaddoura, and T. Zayed, “Condition Prediction for Cured-in-place Pipe Rehabilitation of Sewer Mains,” Journal of Performance of Constructed Facilities, vol. 30, 2016.
[15] I. Bakry, H. Alzraiee, M. Masry, K. Kaddoura, and T. Zayed, “Condition Prediction for Chemical Grouting Rehabiliation of Sewer Networks,” Journal of Performance of Constructed Facilities, vol. 30.
[16] American Society of Civil Engineers, 1988 Infrastructure Report Card. United States, 1988.
[17] American Society of Civil Engineers, 1998 Infrastructure Report Card. United States, 1998.
[18] American Society of Civil Engineers, 2001 Infrastructure Report Card. United States, 2001.
[19] American Society of Civil Engineers, 2003 Infrastructure Report Card. United States, 2003.
[20] American Society of Civil Engineers, 2004 Infrastructure Report Card. United States, 2004.
[21] American Society of Civil Engineers, 2005 Infrastructure Report Card. United States, 2005.
[22] American Society of Civil Engineers, 2009 Infrastructure Report Card. United States, 2009.
[23] American Society of Civil Engineers, 2013 Infrastructure Report Card. United States, 2013.
[24] American Society of Civil Engineers, 2017 Infrastructure Report Card. United States, 2017.
[25] L. K. Chan, and M. L. Wi, “Quality function deployment: a literature review,” European Journal of Operational Research, vol. 143, 2002, pp. 463 – 497.
[26] A. I. A. Costa, M. Dekker, and W. M. F. Jongen, “Quality function deployment in the food industry: a review,” Trends in Food Science & Technology, vol. 11, 2000, pp. 306 – 314.
[27] I. Dikmen, M. T. Birgonul, and S. Kiziltas, “Strategic use of quality function deployment (QFD) in the construction industry,” Building and Environment, vol. 40, 2005, pp. 245 – 255.
[28] C. P. Govers, “What and how about quality function deployment (QFD),” International Journal of Production Economics, vol. 46, 1996, pp. 575 – 585.
[29] G. H. Tzeng, C. H. Chiang, and C. W. Li, “Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL,” Expert Systems with Applications, vol. 32, 2007, pp. 1028 - 1044.
[30] J. P. Davies, B. A. Clarke, J. T. Whiter, and R. J. Cunningham, “Factors influencing the structural deterioration and collapse of rigid sewer pipes,” Urban Water, vol. 3, 2001, pp. 73 – 89.