Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models.
ResumenGómez-Martínez, P., Cubillo-González, F., & Martín-Carrasco, F. J. (julio-agosto, 2017). Metodología para caracterizar la eficiencia de una red de distribución sectorizada. Tecnología y Ciencias del Agua, 8(4), 57-77.Mejorar la eficiencia en las redes de distribución de agua potable, garantizando un nivel de servicio predefinido, es uno de los objetivos principales para los operadores del abastecimiento. Con el fin de mejorar la gestión y el control de las redes existentes se ha ido extendiendo la sectorización, que divide la red en zonas monitorizadas y aisladas mediante válvulas frontera. Ante la diversidad de criterios para el diseño de los sectores, se plantea una metodología de valoración de redes sectorizadas, que permite seleccionar la configuración de sector más eficiente en términos de vulnerabilidad del servicio y costes (también entendido como costos) asociados. La vulnerabilidad se evalúa con una función multiobjetivo con base en tres de los principales objetivos vinculados con el servicio que se persiguen con la sectorización: continuidad del servicio, calidad del agua y cumplimiento de un régimen de presiones adecuado. Se definen una serie de indicadores de cuantificación de estos objetivos, que son normalizados y combinados con referencia a la red de estudio. Para valorar la eficiencia de cada solución, se analizan los indicadores junto con los costes de implantación e instrumentación, energéticos, de operación y mantenimiento para cada alternativa mediante un análisis de Pareto. El análisis de vulnerabilidad permite identificar los sectores donde priorizar las actuaciones en redes existentes; el análisis de eficiencia permite seleccionar la mejor opción entre las distintas alternativas y el diseño de nuevos ámbitos de una red sectorizada. La metodología se ha aplicado en 494 sectores de la red Canal de Isabel II, en Madrid, España.Palabras clave: red de distribución sectorizada, indicadores, eficiencia, vulnerabilidad del servicio, DMA, nivel de servicio. Gómez-Martínez, P., Cubillo-González, F., & Martín-Carrasco, F.J. (July-August, 2017
Aging infrastructuresprovide and maintain a specific level of service to consumers. In this regard, efficient replacement polices are needed. This paper proposes a method for improving renewal efficiency in water supply systems through a reliable asset lifetime model that will lead investments to those elements with greater impact in service provision to the end user. As uncertainties are minimized, the likelihood of failure will be more accurate and renewal investm Therefore, the failure predictor model has been built in a reliable manner through collected data from Madrid distribution network which comprises more than 17.000 km with over 400.000 water pipes. It is based on the statis gathered during four complete years. Additionally, detailed information from more than 4.400 disturbance events was recorded through field visits and laboratory essays of soil and pipe materials when failures were repaired. Examination of such large series of data recorded allows a better understanding of explanatory factors of failures. It is an essential step for building a consistent asset lifetime model. According to this model, a renewa on the risk of service disturbance and involved costs. It supports planning and operation decisions reaching failures reduction and system resiliency improvement.
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