Published

2017-05-01

Identifying explanatory variables of structural state for optimum asset management of urban drainage networks: a pilot study for the city of Bogota

Identificación de factores de riesgo para la gestión patrimonial óptima de sistemas de drenaje urbano: estudio piloto en la ciudad de Bogotá

DOI:

https://doi.org/10.15446/ing.investig.v37n2.57752

Keywords:

CCTV, explanatory variables, sewer asset management, sewer system, structural failure (en)
CCTV, gestión patrimonial, factores de riesgo, fallos estructurales (es)

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Authors

  • H Angarita Pontificia Universidad Javeriana
  • P Niño Pontificia Universidad Javeriana
  • D Vargas Pontificia Universidad Javeriana
  • N Hernández Pontificia Universidad Javeriana
  • A Torres Pontificia Universidad Javeriana

The aim of this work is to identify and quantify physical and environmental explanatory variables for the structural state of urban drainage networks in a pilot study located in Bogota, Colombia. The analysis used information from 2291 CCTV inspections collected by the Water and Sewerage Company of Bogota (EAAB, from its Spanish initials) using tele-operated equipment during 2008-2010. Linear regression models were established to identify the environmental and physical characteristics of the pipes that are significantly associated with the occurrence, magnitude and type of the failures commonly found. Despite the fact that the correlation levels show that the developed model has a very low predictive capacity, it was found that the process of selecting assets for CCTV inspection can be optimized, increasing the success rate in failure detection.

Este artículo presenta los resultados de un estudio piloto realizado en la ciudad de Bogotá para identificar y cuantificar factores de riesgo físicos y/o ambientales de las redes de drenaje urbano, en el marco de un enfoque proactivo de gestión patrimonial de la infraestructura de servicios públicos. El análisis utiliza información de 2291 inspecciones de CCTV recopiladas por la Empresa de Acueducto y Alcantarillado de Bogotá (EAAB) mediante equipos tele-operados durante los años 2008 a 2010. Mediante modelos de regresión lineal se establecieron entre el conjunto de variables recopiladas mediante procesos de inspección por CCTV, aquellas que muestran una asociación estadísticamente significativa con la ocurrencia, magnitud y/o tipo de fallos que típicamente se encuentran en las conducciones, entre otras, material (Gres y P.V.C) y diámetro de la tubería. Los resultados muestran que es posible optimizar los recursos para la inspección de las redes con fines de mejorar la tasa de éxito en la detección de fallos.

References

Ana, E., & Bauwens, W. (2007). Sewer network asset management decision-support tools: a review. In International Symposium on New Directions in Urban Water Management 1-8.

Ana, E. V., & Bauwens, W. (2010). Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods. Urban Water Journal, 7(1), 47-59.

Anbari, M. J., Tabesh, M., & Roozbahani, A. (2017). Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks. Journal of Environmental Management, 190, 91-101.

Baik, H. S., Jeong, H. S. D. and Abraham, D. M., (2006). Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems. Journal of Water Resource Planning & Management, ASCE,

(1), 15–24.

Berardi, L., Giustolisi, O., Savic, D. A., & Kapelan, Z. (2009). An effective multi-objective approach to prioritisation of sewer pipe inspection. Water science and technology, 60(4), 841.

Caradot, N., Kley, G., Kropp, I., Schmidt, T. (2013a). Review of available technologies and methodologies for sewer condition evaluation. SEMA Project Report

Caradot N., Sonnenberg H., Kropp I., Schmidt T., Ringe A., Denhez S., Hartmann A., Rouault P. (2013b). Sewer deterioration modelling for asset management strategies – state-of-the-art and perspective. 5th LESAM - Leading-Edge Strategic Asset Management 2013, 10-23 september, Sydney, Australia.

Cardoso MA, Silva MS, Coelho ST, Almeida MC, Covas DI. (2012). Urban water infrastructure asset management – a structured approach in four water utilities. Water Sci Technol. 2012; 66(12):2702-11. DOI: 10.2166/wst.2012.509.

Chughtai, F., & Zayed, T. (2008). Infrastructure condition prediction models for sustainable sewer pipelines. Journal of Performance of Constructed Facilities, 22(5), 333-341.

Davies, J.P., Clarke, B.A., Whiter, J.T., & Cunninghan, R.J. (2001a). Factors influencing the structural deterioration and collapse of rigid sewer pipes. Urban water , 3(1-2), 73–89.

Davies, J.P., Clarke, B.A., Whiter, J.T., Cunninghan, R.J. & Leidi, A. (2001b). The structural condition of rigid sewer pipes: a statistical investigation. Urban Water, 3(4), 277–286.

Dirksen, J., Clemens, F. H. L. R., Korving, H., Cherqui, F., Le Gauffre, P., Ertl, T., ... & Snaterse, C. T. M. (2013). The consistency of visual sewer inspection data. Structure and infrastructure

engineering, 9(3), 214-228.

Empresa de Acueducto y Alcantarillado de Bogotá EAAB. (2001). “NS – 058. Aspectos Técnicos para inspección y mantenimiento de redes y estructuras de alcantarillado”, Bogotá, Colombia, EAAB-E.S.P.: 2001

Fenney S., C., 2009. Condition Assessment of Wastewater Collection Systems White Paper on Condition Assessment of Wastewater Collection Systems.

Hao, T., Rogers, C. D. F., Metje, N., Chapman, D. N., Muggleton, J. M., Foo, K. Y., ... & Parker, J. (2012). Condition assessment of the buried utility service infrastructure. Tunnelling and Underground Space Technology, 28, 331-344.

Hernández, N., Neira, N. O., & Torres, A. (2016). Identificación de factores de tipo categórico relacionados con la condición estructural de tuberías de alcantarillado de Bogotá a partir de conceptos de entropía de la información. Ingeniería solidaria, 12(19), 63-71.

Koo, D.-H. & 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., & Desmulliez, J. J. (2007). Performance indicators and multicriteria decision support for sewer asset management. Journal of Infrastructure Systems,13(2), 105-114.

Liu, Z., & Kleiner, Y. (2013). State of the art review of inspection technologies for condition assessment of water pipes. Measurement, 46(1), 1-15.

López-Kleine, L., Hernandez, N., & Torres, A. (2016). Physical characteristics of pipes as indicators of structural state for decision-making considerations in sewer asset management. Ingeniería e Investigación, 36(3), 15-21.

Perez P., M.A., Perdomo G., M.C. & Torres, A., 2011. Herramientas para la toma de decisiones en la gestión de alcantarillados: prospectivas de implementación en la EAAB. Revista Acodal ISSN: 0120-0798, 228(1), 25–36.

Renaud, E., De Massiac, J. C., Bremond, B., & Laplaud, C. (2009). SIROCO, a decision support system for rehabilitation adapted for small and medium size water distribution companies. In Strategic Asset Management of Water Supply and Wastewater Infrastructures (pp. 329-344). IWA Publishing London.

Saegrov, S. & Schilling, W., 2005. Computer Aided Rehabilitation of sewer and storm water networks. In Proceedings of the ninth international conference on urban drainage.

Salman, B., 2010. Infrastructure Management and Deterioration Risk Assessment of Wastewater Collection Systems. University of Cincinnati.

Salman, B. & Salem, O., 2012. Modeling failure of wastewater collection lines using various section-level regression models. Journal of Infrastructure Systems.

Sinha, S. K., & McKim, R. A. (2007). Probabilistic based integrated pipeline management system. Tunnelling and underground space technology, 22(5), 543-552.

Tagherouit, W. B., Bennis, S., & Bengassem, J. (2011). A fuzzy expert system for prioritizing rehabilitation of sewer networks. Computer-Aided Civil and Infrastructure Engineering, 26(2), 146-152.

Younis, R. & Knight, M.A., 2010. A probability model for investigating the trend of structural deterioration of wastewater pipelines. Tunnelling and Underground Space Technology, 25(6), 670–680.

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How to Cite

Angarita, H., Niño, P., Vargas, D., Hernández, N., & Torres, A. (2017). Identifying explanatory variables of structural state for optimum asset management of urban drainage networks: a pilot study for the city of Bogota. Ingeniería E Investigación, 37(2), 6-16. https://doi.org/10.15446/ing.investig.v37n2.57752