The influence of condition assessment uncertainties on sewer deterioration modelling

Caradot, Nicolas; Riechel, Mathias; Rouault, Pascale; Caradot, Antoine; Lengemann, N.; Eckert, Elke; Ringe, Alexander; Clemens, F.H.L.R.; Cherqui, Frédéric

doi:10.1080/15732479.2019.1653938

Cited by 16 publications

(7 citation statements)

References 44 publications

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“…It should be noted that during operation, depending on the catchment type, substantial amounts of suspended solids and resultant sediments of various roughness coefficient may occur and flow in the stream, which in the long-term changes the average roughness of the pipe walls [155]. Moreover, it should be remembered that, depending on the local conditions, biofilm may cover the pipe walls, and the deposits and sediments on the pipe bottom can reduce its cross section and change the flow conditions, which affects the values of parameters identified in the hydrodynamic model [156][157][158]. This is extremely difficult to determine, because CCTV inspection of sewer pipes should be performed after each rainfall event, which would provide reliable data about the pipe's physical and hydraulic characteristics.…”

Section: Surface Roughness and Runoff Coefficientsmentioning

confidence: 99%

Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments

Szeląg

Łagód

Musz-Pomorska

et al. 2022

Water

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Modelling of stormwater networks and the related object (combined sewer overflows, diversion chambers, retention tanks) is a complex task requiring colleting of data with appropriate time and spatial resolution as well as application of adequate models. Often there is a need to find balance between the costs of conducting measurement (period, resolution) and the uncertainty of the model results. This paper presents an overview of simulation tools for sewerage networks modelling, related objects, as well as low-impact development (LID) systems in relation to the hydrodynamic and statistical models. Consecutive stages of data collection, sources of data uncertainty, limitations resulting from the adopted measurement methodology, as well as their influence on the simulation results and possible decision-making using the developed hydrodynamic or statistical model, are discussed. Attention is drawn to the optimization methods enabling reduction in the uncertainty of statistical models. The methods enabling the analysis of model uncertainty, as well as evaluation of its influence on the calculation results pertaining to stormwater hydrographs, retention tank capacity and combined sewers overflows, are also discussed. This is a very important aspect in terms of optimizing construction works in the sewerage network and designing their appropriate dimensions to achieve the assumed hydraulic effects.

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Section: Surface Roughness and Runoff Coefficientsmentioning

confidence: 99%

Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments

Szeląg

Łagód

Musz-Pomorska

et al. 2022

Water

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“…Cada elemento de ponderación, a su vez, se podría analizar con técnicas de predicción aplicando "machine learning" (por ejemplo, véase el estudio sobre predicción de roturas aplicando la regresión logística de (Robles-Velasco et al 2020)). Otros investigadores se centran en simular tasas de deterioro a partir de modelos estadísticos (Ouellet & Duchesne 2018), que se pueden utilizar para pronosticar la evolución del estado de la red de alcantarillado bajo diferentes estrategias de inversión (Caradot et al 2020).…”

Section: Modelo Del Proceso De Toma De Decisionesunclassified

Modelo de gestión de activos para empresas de infraestructuras hídricas

Ruíz¹,

Onieva²,

Muñuzuri³

et al. 2022

DyO

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La gestión de los activos y su renovación generalmente se planifican a nivel anual y operativo. Sin embargo, a nivel estratégico y táctico, las empresas de infraestructuras buscan modelos de gestión que permitan analizar sus activos, su evolución y la detección de aquéllos que deben ser renovados e incorporados antes en las planificaciones a medio y largo plazo. Además, es necesario definir el esfuerzo financiero y su aplicación temporal. Dicho esfuerzo debe permitir que la cartera de activos que conforman la red hídrica esté alineada con los objetivos de gestión de activos de la organización. El seguimiento de los objetivos, entre ellos el mantenimiento sostenible de la infraestructura de red, se puede desarrollar en base a indicadores de gestión que faciliten su renovación. Los indicadores permiten analizar el estado o fortaleza de la organización y constituyen un factor de oportunidad para mejorar la eficiencia en la administración de los activos cuando se combinan las estrategias financieras y las no financieras.

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“…Structural condition models can be classified into physical, statistical, and machine learning [11][12][13][14]. In the physical models, parameters related to structures of the sewer pipes (e.g., material, diameter, type of effluent, etc.,) are employed to fit mathematical equations to the sewer's status [15].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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Assessment of the structural condition of sewer pipes is one of the critical steps in asset management and support investment decisions; therefore, structural condition models with high accuracy are important that can help the utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of advanced machine learning (ML) for predicting the structural condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater level, traffic volume, distance from the road, and soil type) were used to assess the sewer structural condition employing seventeen ML models. After processing the sewer inspections, 1,159 of 1,449 individual pipelines were used to train the structural condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the RF model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

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The influence of condition assessment uncertainties on sewer deterioration modelling

Cited by 16 publications

References 44 publications

Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments

Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments

Modelo de gestión de activos para empresas de infraestructuras hídricas

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

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