Tools for the implementation of proactive maintenance in urban sewer systems by the use of flooding reliability and entropy of information concepts
DOI:
https://doi.org/10.17533/udea.redin.14226Keywords:
decision trees, flooding reliability, prevnetive maintence, information entropy, urban sewerage managementAbstract
The existing urban sewer databases in cities of developing countries present deficiencies in the records of the physical characteristics of the pipes, which limits their use as support for the implementation of management tools for said infrastructure. The objective of this work consisted in proposing and using in 13 sub-basins belonging to the El Salitre basin of Bogotá, comprising an area of 4515 Ha, with a total of 12,842 pluvial and combined pipes, decision trees and information entropy to identify pipes with demands for network maintenance activities such as cleaning, rehabilitation, replacement, etc., based on estimated flood reliability (for 2337 pipes whose physical characteristics were accurately recorded). The classificatory model presented an average predictive capacity of 62%. The pipes were classified using 28 rules, according to the reliability values, identifying 3383 pipes (26% of the total) with a high prioritization of proactive maintenance.
Downloads
References
E. Ana, W. Bauwens. “Sewer Network Asset Management Decision-Support Tools: A Review”. International Symposium on New Directions in Urban Water Management. 2007. París: UNESCO. pp. 1-8.
S.T. Ariaratnam, C.W. MacLeod. “Financial outlay modeling for a local sewer rehabilitation strategy Financial Outlay modeling for Local Rehabilitation Strategy”. Journal of Construction Engineering and Management 2002;128(6):486-495 Journal of Construction Engineering and Management. 2002. pp. 486-496. DOI: https://doi.org/10.1061/(ASCE)0733-9364(2002)128:6(486)
S. Arthur, H. Crow, L. Pedezert, N. Karikas. “The holistic prioritization of proactive sewer maintenance”. Water Science & Technology. Vol. 59. 2009. pp. 1385 - 1396. DOI: https://doi.org/10.2166/wst.2009.134
M. Aulinas, J. Nieves, U. Cortes, M. Poch “Supporting decision making in urban wastewater system using a knowledge-based approach” 2011 Environmental Modelling and Software. Vol. 26. pp. 562-572. DOI: https://doi.org/10.1016/j.envsoft.2010.11.009
H. Baik, H. Jeong, D. Abraham, “Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems”. Journal of Water Resources Planning and Management. Vol. 132. 2006. pp. 15-24. DOI: https://doi.org/10.1061/(ASCE)0733-9496(2006)132:1(15)
R. Baur, R. Herz. Selective Inspection Planning with Aging Forecast for Sewer Types. Efficient Water Management. 2001. pp. 389-396. DOI: https://doi.org/10.2166/wst.2002.0704
S. Bennis, J. Bengassem, P. Lamarre. “Hydraulic Performance Indez of a Sewer Network”. Journal of Hydraulic Engineering. Vol 129. 2003. pp. 504-510. DOI: https://doi.org/10.1061/(ASCE)0733-9429(2003)129:7(504)
N. Caradot, D. Granger, J. Chaogier, F. Cherqui, B. Chocat. “Urban flood risk assessment using sewer flooding databases”. Water Science and Technology. Vol. 64. 2011. pp. 832 – 840. DOI: https://doi.org/10.2166/wst.2011.611
J. Chen, A. Hill, L. Urbano “A GIS-based model for urban flood inundation”. Journal of Hydrology. Vol. 373. 2009. pp. 184-192. DOI: https://doi.org/10.1016/j.jhydrol.2009.04.021
T. Cover, A. Joy. Entropy, relative entropy and mutual information. Elements of information theory (2nd Ed). John Wiley & Sons, New Jersey. 2006. pp. 748.
P. Davis, S. Dhammika, D. Marlow, M. Mogolia, S. Gould, S. Burn. Failure Prediction and Optimal Scheduling of Replacements in Asbesto Cement Water Pipes. Australia: IWA Publishing. 2008. pp. 239-252. DOI: https://doi.org/10.2166/aqua.2008.035
R. Fenner. “Approaches to sewer maintenance: A review”. Urban Water. Vol. 2. 2000. pp. 343-356. DOI: https://doi.org/10.1016/S1462-0758(00)00065-0
D. Fernandez, M. Lutz. “Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis”, Engineering Geology. Vol. 111. 2010. pp. 90-98. DOI: https://doi.org/10.1016/j.enggeo.2009.12.006
M. Hall, F. Frank, G. Holmes, B. Fahringer, P. Reutemann, I. Witten. “The WEKA Data Mining Software: An Update”. SIGKDD Explorations. Vol. 11. 2009. pp. 10-18. DOI: https://doi.org/10.1145/1656274.1656278
Z. Liu, Y. Kleiner. “State of Art Review of Inspection Technologies for Condition Assessment of Water Pipes”, Measurement. 2012. http://dx.doi.org/10.1016/j.measurement.2012.05.032 (Revisado Julio 2012) DOI: https://doi.org/10.1016/j.measurement.2012.05.032
T. Micevski, G. Kuczera, P. Coombes. “Markov Model for Storm Water Pipe Deterioration”. Infrastructure Systems. Vol. 8. 2002. pp. 49-56. DOI: https://doi.org/10.1061/(ASCE)1076-0342(2002)8:2(49)
J. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco. 1993. pp. 302.
A. Quintero, D. Konare, S. Pierre. “Prototyping an Intelligent Decision Support System for improving urban infrastructures management”. European Journal of Operational Research. Vol. 162. 2005. pp. 654-672. DOI: https://doi.org/10.1016/j.ejor.2003.10.019
J. Rodríguez, M. Díaz, J. Penagos, N. McIntyre, C. Maksimovié. “A serviceability model for supporting proactive maintenance in urban drainage systems”. Proceedings of the 12th International Conference on Urban Drainage. Porto Alegre, Brazil. 11-16 September 2011.
J. Rodríguez, N. McIntyre, M. Díaz, C. Maksimovié. “A database and model to support proactive management of sediment related sewer blockages”, Water Research (2012), http://dx.doi.org/10.1016/j.watres.2012.06.037 (Revisado Julio 2012) DOI: https://doi.org/10.1016/j.watres.2012.06.037
L. Rokach. O. Maimon. Data mining with decision trees: theory and applications. World Scientific Publishing, Singapore. 2008. pp. 244. DOI: https://doi.org/10.1142/6604
J. Ruwanpura, S. Ariaratnam, A. El-Assaly. “Prediction models for sewer infrastructure utilizing rule-based simulation”. Civ. Eng. Environ. Syst. Vol. 21. 2004. pp. 169-185. DOI: https://doi.org/10.1080/10286600410001694192
S. Saegrov. CARE-S: Computer Aided Rehabilitation of Sewer and Storm Water Networks. Londres: IWA Publishing. 2006. pp. 160.
B. Salman, O. Salem. “Modeling Failure of Wastewater Collection Lines Using Various Section-Level Regression Models.” J. Infrastruct. Syst. Vol. 18. 2012. pp. 146-154. DOI: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000075
S. Sandoval, A. Torres, K. Navarro. Estimación de la confiabilidad de inundación en alcantarillados pluviales de Bogotá mediante métodos de generación aleatoria. Evento: Agua 2011 ECOSISTEMAS Y SOCIEDAD, Seminario Internacional: “Inundaciones, Escasez de Agua y Ecosistemas, Acciones Frente al Cambio Climático”. Cali. Colombia. 2011.
S. Stone, E. Dzuray, D. Meisegeier, A. Dahlorg. Decision-Support Tools for Predicting the Performance of Water Distribution and Wastewater Collection Systems. US EPA. 2002. pp. 97.
W. Tagherouit, S. Bennis, J. Bengassem. “A Fuzzy Expert System for Prioritizing of Sewer Networks”. Computer-Aided Civil and Infrastructure Engineering, Vol. 26. 2011. pp. 146-152. DOI: https://doi.org/10.1111/j.1467-8667.2010.00673.x
A. Torres, S. Sandoval, K. Navarro, L. Pulido. Strategies of maintenance in storm sewers in Bogotá Colombia by using estimates of operational and flooding reliabilities. Proceedings of the 12th International Conference on Urban Drainage. Porto Alegre. Brazil. 11-16 September. 2011. pp. 9.
A. Torres, Z. Méndez. Estimación del riesgo en estructuras de drenaje pluvial mediante herramientas de inteligencia artificial. Seminario Internacional: “La Hidroinformática en la Gestión Integrada de los Recursos Hídricos”. Ponencia: Libro: Agua 2003. Cartagena de Indias. Colombia. pp. 31-38.
T. Veldhuis, J. Clemens, F. Clemens. “The efficiency of asset management strategies to reduce urban flood risk”. Water Science & Technology. Vol. 64. 2011. pp. 1317-1324. DOI: https://doi.org/10.2166/wst.2011.715
M. Yang, T. Su. “Automated diagnosis of sewer pipe defects based on machine learning approaches”. Expert Systems with Applications (2007). doi:10.1016/j.eswa.2007.08.013 DOI: https://doi.org/10.1016/j.eswa.2007.08.013
Q. Zhou, P. Mikkelsen, K. Halsnaes, K. Arnbjerg. “Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits”. Journal of Hydrology, Vol. 414- 415. 2012. pp. 539-549. DOI: https://doi.org/10.1016/j.jhydrol.2011.11.031
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Revista Facultad de Ingeniería
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.
Twitter