The week's pick
Reseach Article
A New Image Model for Predicting Cracks in Sewer Pipes based on Time
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 87 - Number 9 |
| Year of Publication: 2014 |
| Authors: Iraky Khalifa, Amal Elsayed Aboutabl, Gamal S Abdel Aziz Barakat |
10.5120/15238-3779
|
Iraky Khalifa, Amal Elsayed Aboutabl, Gamal S Abdel Aziz Barakat . A New Image Model for Predicting Cracks in Sewer Pipes based on Time. International Journal of Computer Applications. 87, 9 ( February 2014), 25-32. DOI=10.5120/15238-3779
Abstract
Sewer overflows may cause communities to be vulnerable to various health problems and other monetary losses. This puts a lot of burden on responsible to minimize end user complaints. Therefore, crack prediction would be helpful to facilitate decision makers to control sewer overflow problems and prioritize inspection and rehabilitation needs . The accurate prediction of current underground sewer pipe cracks must be done before any pipe crashing with enough period of time to enable rehabilitation and replacement intervals, appropriate remedial methods and preventing sewer pipes crashing. Unfortunately, traditional technologies and models approaches have been limited to predict the development of sewer pipe cracks. In this paper, we address the problem of crack prediction of such cracks. This paper provides a proposed model which predict crack and cracks developments in any period of time that may occur in weak areas of a network of pipes. . We evaluate our results by comparing them with those obtained by many other models. The accuracy percentage of this model exceeds 90% and outperforms other approaches.
References
- Ruwanpura J, Ariaratnam, S, and El-Assaly, A, (2004), "Prediction Models for Sewer Infrastructure Utilizing Rule-Based Simulation", Journal of Civil Engineering and Environmental Systems, volume 21, No 3, Page 169-185
- Holding company for water and waste water, Japanese Agency for International Cooperation ,'General master plan for Cairo & El Gisa governorates' ,2013 http://www. cairofuturevision. gov. eg/Uploads/IssueUpload/45/%D8%A7%D9%84%D9%85%D9%8A%D8%A7%D9%87%20%D9%88%D8%A7%D9%84%D8%B5%D8%B1%D9%81%20%D8%A7%D9%84%D8%B5%D8%AD%D9%8A. pdf
- Ministry of Finance :PPP Central Unit, retrieved on July 23, 2011, http://www. almasryalyoum. com/News/Index?tag=159433.
- Fenner, R. A. (2000). Approaches to sewer maintenance: a review. Urban Water, 2(4), 343–356.
- Moselhi, O and Shehab-Eldeen, T, (2000), "Classification of Defects in Sewer Pipes using Neural Networks", ASCE Journal of Infrastructures System, Volume 06, Number 03, September, 2000
- Chae, M and Abraham, M, (2001), "Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment", ASCE Journal of Computing in Civil Engineering, Volume 15, Number 1, January, 2011
- Hasegawa, K, Wada, Y & Miura, H, (1999), "New Assessment System for Premeditated Management and Maintenance of Sewer Pipe Networks", Proceedings of 8th International Conference on Urban Storm Drainage, Page 586-593, Sydney, Australia
- Ariaratnam, S, El-Assaly, A & Yang, Y, (2001), "Assessment of Infrastructure Inspection Needs using Logistic Models", ASCE Journal of Infrastructure Systems, Volume 7, No 4, December 2001
- McDonald, S & Zhao, J, (2001), "Condition Assessment and Rehabilitation of Large Sewers", Proceedings of International Conference on Underground Infrastructure Research, page 361-369, Waterloo, Canada
- Baur, R & Herz, R, (2002), "Selective Inspection Planning with Ageing Forecast for Sewer Types", International Water Association (IWA) Journal of Water Science and Technology, Volume 46, No 6-7, page 389-396
- Yan J & Vairavamoorthy, K, (2003), "Fuzzy Approach for Pipe Condition Assessment", Proceedings of the American Society of Civil Engineers (ASCE) International Pipeline Conference, USA
- Ruwanpura J, Ariaratnam, S, and El-Assaly, A, (2004), "Prediction Models for Sewer Infrastructure Utilizing Rule-Based Simulation", Journal of Civil Engineering and Environmental Systems, volume 21, No 3, Page 169-185
- Najafi M & Kulandaivel G, (2005), "Pipeline Condition Prediction Using Neural Network Models", Proceedings of the American Society of Civil Engineers (ASCE) International Pipeline Conference, USA
- Kompetenzzentrum Wasser Berlin, " REVIEW OF SEWER DETERIORATION MODELS Cicerostraße 24, 10709 Berlin, Germany,2013
- PIKS Scientific Inside, WILLIAM K. PRATT ,DIGITAL IMAGE PROCESSING, Los Altos, California, A John Wiley & Sons, Inc,2007
- Sinha, S. K. , & Fieguth, P. W. (2006). Segmentation of buried concrete pipe images. Automation in Construction, 15(1), 47–57.
- Dingus, M. , Haven, J. , and Russell, A. _2002_. Nondestructive, noninvasive assessment of underground pipelines, AWWA Research Foundation, Denver
- Yan, H. (1996). Unified formulation of a class of image thresholding techniques. Pattern Recognition, 29(12), 2025–2032
- Umbaugh Scot E, Computer Vision and Image Processing, Prentice Hall, NJ, 1998, ISBN 0-13-264599-8
- Gonzalez and Woods Prentice Hall ,Errata and Clarifications Digital Image Processing 3rd Edition © 2008 September 10, 2008 CORRECTIONS
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