Sewer asset management – state of the art and research needs

Tscheikner-Gratl, Franz; Caradot, Nicolas; Cherqui, Frédéric; Leitão, João P.; Ahmadi, Mehdi; Langeveld, Jeroen; Gat, Yves Le; Scholten, Lisa; Roghani, Bardia; Rodríguez, Juan ́Pablo; Lepot, Mathieu; Stegeman, Bram; Heinrichsen, Anna; Kropp, Ingo; Kerres, Karsten; Almeida, M. C.; Bach, Peter M.; Vitry, Matthew Moy de; Marques, Alfeu Sá; Simões, Nuno; Rouault, Pascale; Hernández, Nathalie; Torres, Andrés; Werey, Caty; Rulleau, Bénédicte; Clemens, F.H.L.R.

doi:10.1080/1573062x.2020.1713382

Cited by 92 publications

(74 citation statements)

References 109 publications

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“…For example, behaviors such as littering or improper disposal of clogging substances in sewers can significantly increase flood risk (Alda‐Vidal et al., 2020; Armitage, 2007), while infrastructure maintenance can play a key role in flood mitigation. Simulation of these processes using the parameterizations provided in existing UFMs remains challenging (Tscheikner‐Gratl et al., 2019).…”

Section: Limitations Of Existing Urban Flood Modeling Frameworkmentioning

confidence: 99%

The Value of Urban Flood Modeling

et al. 2021

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As the human population and the impending magnitude of global climate change both continue to grow, there is an imperative to better understand the function of cities as ecosystems and their resilience to climate-driven disturbances. Floods are prominent disturbances to urban ecosystems (Grimm et al., 2017; Rentschler et al., 2019), and their meteorological drivers-including extreme precipitation, sea level rise, and coastal storms-are projected to increase in coming decades (Y. Chen et al., 2018). Numerical models are essential tools for understanding floods and their associated risks. But despite recent advances in computational resources, existing flood models remain limited in their capability to represent integrated flooding processes in urban areas and provide credible, quantitative information needed to support risk assessment and resilience practice. In this commentary, we discuss the potential value of urban flood modeling for urban resilience and limitations of existing modeling approaches. We then present a research agenda and vision for the future of urban flood modeling, realized through collaboration between researchers and practitioners. We define urban flood models (UFMs) as numerical models that are capable of representing the features of urban ecosystems and the mechanisms of flooding that impact them. Cities are Social-Ecological-Technological

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Section: Limitations Of Existing Urban Flood Modeling Frameworkmentioning

confidence: 99%

The Value of Urban Flood Modeling

et al. 2021

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“…A sewer system is a hidden but very expensive type of infrastructure to maintain [1]. Breakdown of a sewer can result in significant damage to roads and buildings.…”

Section: Introductionmentioning

confidence: 99%

“…Breakdown of a sewer can result in significant damage to roads and buildings. Furthermore, reduced functionality of the sewers can lead to flooding and exfiltration, for example, which can affect a number of externalities, such as property, traffic disruption, public health and the environment [1,2]. For these reasons, the sewers' operators need to replace them in a timely manner, especially if the sewers are critical.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Feature Analysis for Sewer Deterioration Modeling

Hansen

Rasmussen²,

Uggerby³

et al. 2021

Water

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Timely maintenance of sewers is essential to preventing reduced functionality and breakdown of the systems. Due to the high costs associated with inspecting a sewer system, substantial research has focused on sewer deterioration modeling and identification of the most useful features. However, there is a lack of consensus in the findings. This study investigates how the feature importance depends on the definition of bad pipes and how the feature importance changes between utilities with similar data bases. A dataset containing 318,457 pipes from 35 utilities with a condition state (CS) ranging from one to four was used. The dataset was cleaned, and a backward step analysis (BSA) was applied to two ways of binarizing the CS. Additionally, a BSA was applied for each utility with ≥100 pipes in CS four. The results showed that a selective definition of bad pipes reduced the performance and changed the order of which features contributed the most. In each case, either year of construction, age, groundwater, year of rehabilitation, or dimension was the most important feature. On average 6.5 features contributed to the utility-specific models. The feature analysis was sensitive to the inspection strategy, the size of the dataset, and interdependency between the features.

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“…In the last few decades, we have seen a rapid expansion of water-related infrastructure associated with the expansion of cities. The focus is now shifting from new design and construction to redesign and asset management (Tscheikner-Gratl et al 2020). Medium to long-term operation of infrastructure is becoming a priority for all infrastructure, and the economic context that many industrial countries are facing will reinforce the shift from replacement to management (ASCE 2011).…”

Section: Introductionmentioning

confidence: 99%

A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring

et al. 2020

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Water infrastructure in cities is complex and requires proactive management to optimise function. The scale and distribution of assets across municipalities requires affordable systems which can trigger alerts. Systems underpinned by low-cost sensors could meet increasing monitoring needs: more assets, more often, and at a better resolution. However, low-cost sensors require appropriate testing to assess their performance and optimise their use. Here, we focus on low-cost water level sensors, often considered as the main monitoring parameters for water-related infrastructures. We developed a platform and testing protocol to assess the suitability of low-cost sensors. We assessed the performance of three widely used low-cost sensors: laser-ranging, ultrasonic-ranging, and pressure. Our main results showed that the ultrasonic sensor offers the best price to accuracy ratio, and the pressure sensor provides the highest accuracy while still at a very low cost. Our platform and protocol provide a standardised testing and calibration method which can be applied to any sensor. The platform can be used to gather and share results, to enhance community knowledge and encourage the use of new (low-cost or not) sensors. The development of low-cost sensors is an important step toward the wider use monitoring systems for water infrastructure.

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Sewer asset management – state of the art and research needs

Cited by 92 publications

References 109 publications

The Value of Urban Flood Modeling

The Value of Urban Flood Modeling

Comprehensive Feature Analysis for Sewer Deterioration Modeling

A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring

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