Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.
Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe, whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.
Several deterioration models have been used to predict the structural condition of sewer pipes, and some have been applied in different cities in the world. However, each one of these models has not been proved simultaneously for case studies with different characteristics (topographic conditions, soil uses, demographic growth, utilities' service operation and city's dynamic) and the use of their predictions have not been analyzed to support different management objectives. Therefore, the objective of this work was to assess the prediction results of two models (based on Logistic Regression and Random Forest (RF) methods), which previously have been identified as successful in other experiences, for two different case studies (a city in Colombia and a city in Germany). The prediction assessment was carried out by three analysis techniques (Positive Likelihood Rate (PLR) index, performance curve and deviation analysis). According to the results, we found that: (i) the model based on RF was the one that could be useful as a support tool in the sewer asset management of both case studies; (ii) for the German city, the prediction results could be useful for designing strategic investment plans in order to know the number of pipes that the utility should rehabilitate each year; and (iii) for the Colombian city, the predictions are appropriate to make decisions concerning inspection or rehabilitation plans, since the probability of identifying the sewer's assets in critical condition (C4) correctly (according to the analysis of the sample of the 10% of sewers with the highest probability to be in this condition) is around 63% and could be 83% if the stakeholders also consider in these plans the misclassification of those pipes in a bad structural condition (C3).
Sewer deterioration is a problem that affects many cities of the world. This affects the structural state of the sewer systems, as well as its hydraulic capacity and the service level. As a consequence, the sewer system stakeholders are working on the development of a proactive sewer management to make decision in time and avoid public emergencies. Therefore, the objective of this work was to predict the variable state using a clustering algorithm (k-means) in Bogotá's sewer pipes based on its physical characteristics. Among the most representative results was to find a relationship between pipes' characteristics and their structural state (chi-squared). Furthermore, the slope and ground level variables were the most related ones to the state of the pipes. The detected relationships are linear and can be used to make management decisions when pipes are clustered and the clusters are mapped on a principal component plane.Keywords: k-means, sewer asset management, cluster analysis, principal components analysis (PCA), proactive sewer management, sewer pipes, structural pipes state, Bogota's sewer system. RESUMENEl deterioro de los sistemas de alcantarillado es un problema que afecta a las ciudades, no solo en su estado estructural sino también en su capacidad hidráulica y nivel de servicio. En consecuencia, los encargados del sistema de alcantarillado están trabajando en el desarrollo de una gestión proactiva para tomar decisiones a tiempo y evitar emergencias públicas. Es por esto que el objetivo de este trabajo fue predecir la condición de las tuberías en la ciudad de Bogotá utilizando algoritmos tipo cluster (k -means), para discriminar las tuberías que tienen buena condición estructural de las que no. Entre los resultados más sobresalientes se encontró una relación entre las características estructurales de las tuberías y su estado (prueba Chi -cuadrado) siendo la pendiente y la profundidad las variables más relacionadas con el estado de las tuberías. Adicionalmente, estas relaciones encontradas resultaron lineales al agrupar las tuberías en un plano de componentes principales.Palabras clave: k-means, gestión de sistemas de alcantarillado, cluster, análisis de componentes principales (ACP), gestión proactiva de alcantarillados, tuberías de alcantarillado, condición estructural de tuberías de alcantarillado, sistema de alcantarillado de Bogotá.
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