The relevance of sewer deterioration modelling to support asset management strategies

Caradot, Nicolas; Sonnenberg, Hauke; Kropp, Ingo; Ringe, Alexander; Denhez, Stephane; Hartmann, Andreas; Rouault, Pascale

doi:10.1080/1573062x.2017.1325497

Cited by 39 publications

(29 citation statements)

References 23 publications

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“…Harvey and McBean [19] found a tendency towards poor condition after a pipe length of 33 m. In contrast, Khan et al [7] found that pipes over 70 m in length were in good condition more often than shorter pipes, and the results by Baik et al [36] indicated that longer pipes were less likely to deteriorate than shorter ones. Installation depth was found to be insignificant for pipe condition in the work of Ariaratnam et al [9] and Ana et al [10], and significant in Sousa et al [16] and Caradot et al [3]. In the network we analysed, installation depths between 2 m and 3 m were least often connected with poor condition.…”

Section: Discussionmentioning

confidence: 69%

“…Pipe diameter had no effect on pipe condition in the study by Ana et al [10]. In many cases, however, diameter has been identified as a significant factor, e.g., [3,12,33,37]. Khan et al [7] found smaller diameters to be more stable as compared to the larger diameters.…”

Section: Discussionmentioning

confidence: 87%

“…Pipe age has been found influential in various studies (e.g., [3,9,10,12,14,16,33,34]). In the network we studied, there was a clear tendency towards poor condition after a pipe age of 44 years.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies, e.g., [3,[10][11][12]33,37,38] have found that material affects pipe condition, although some studies have suggested the opposite [9]. Khan et al [7] found substantial differences in pipes of different types of concrete, and Syachrani et al [18] found essential differences between the deterioration of vitrified clay pipes and PVC pipes.…”

Section: Discussionmentioning

confidence: 99%

“…In the simplest case, they support short-term decisions on whether or not to change a pipe. If the results are further processed and analysed, they can also provide support for mid-and long-term asset management decisions (e.g., [1][2][3]).…”

Section: Introductionmentioning

confidence: 99%

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Sewer Condition Prediction and Analysis of Explanatory Factors

Laakso

Kokkonen

Mellin

et al. 2018

Water

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Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.2 of 17 different pipe attributes and environmental variables affect pipe condition provides an understanding of the mechanisms that cause poor condition. This provides valuable information not only for inspection decisions, but also for future installations. This study focuses on modelling the prevailing condition of a pipe network, and hence lifespan models are discussed only when they address factors explaining pipe deterioration.The methods previously applied for condition modelling include both traditional statistical methods and machine learning methods. Salman and Salem [8] compared the performance of binary logistic regression, multinomial logistic regression, and ordinal regression in modelling sewer condition. They found ordinal regression to be unsuitable for the study, while binary logistic regression provided the best results. Ariaratnam et al. [9], Ana et al. [10], and Fuchs-Hanusch et al. [11] also applied logistic regression to model poor conditions. Chughtai and Zayed [12] applied multiple regression to model a five-level sewer condition scale. They created four different models: three for predicting the structural condition of different materials and one for predicting the operational condition. Savic et al. [13] and Savic et al. [14] applied a different form of regression, a data-driven modelling algorithm called evolutionary polynomial regression, to model the blockage and collapse events in different pipe classes. Khan et al. [7], Tran et al. [15], and Sousa et al. [16] applied neural networks to model how different variables affect the condition of the sewer. Tran et al. [15] modelled the structural condition of stormwater pipes, while Khan et al. [7] modelled sewer condition and Sousa et al. [16] the condition of sanitary sewers. Sousa et al. [16] compared the performance of artificial neural networks (ANNs) and support vector machines (SVMs) with that of logistic regression. They found that ANNs provided the highest classification performance. SVMs, on the other hand, provided excellent results in the study by Mashford et al. [17], who predicted sewer condition using a five-level scale. Decision trees were applied by Syachrani et al. [18] and Harvey and McBean [19], and random forests by Harvey and McBean [20]. Syachrani et al. [18] found that decision trees consistently outperformed the regression and neural networks in ...

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sewer Condition Prediction and Analysis of Explanatory Factors

Laakso

Kokkonen

Mellin

et al. 2018

Water

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Optimizing SVM Model as Predicting Model for Sewer Pipes in the Two Main Cities in Colombia

Hernández

Caradot

Sonnenberg

et al. 2018

New Trends in Urban Drainage Modelling

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Harmonized assessment of nutrient pollution from urban systems including losses from sewer exfiltration: a case study in Germany

Nguyen

Venohr

2021

Environ Sci Pollut Res

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A growing literature indicates that untreated wastewater from leaky sewers stands among major sources of pollution to water resources of urban systems. Despite that, the quantification and allocation of sewer exfiltration are often restricted to major pipe areas where inspection data are available. In large-scale urban models, the emission from sewer exfiltration is either neglected (particularly from private sewers) or represented by simplified fixed values, and as such its contribution to the overall urban emission remains questionable. This study proposes an extended model framework which incorporates sewer exfiltration pathway in the catchment model for a better justified pollution control and management of urban systems at a nationwide scale. Nutrient emission from urban areas is quantified by means of the Modelling of Nutrient Emissions in River Systems (MONERIS) model. Exfiltration is estimated for public and private sewers of different age groups in Germany using the verified methods at local to city scales, upscaling techniques, and expert knowledge. Results of this study suggest that the average exfiltration rate is likely to be less than 0.01 L/s per km, corresponding to approximately 1 mm/m/year of wastewater discharge to groundwater. Considering the source and age factors, the highest rate of exfiltration is defined in regions with significant proportions of public sewers older than 40 years. In regions where public sewers are mostly built after 1981, the leakage from private sewers can be up two times higher than such from public sewers. Overall, sewer exfiltration accounts for 9.8% and 17.2% of nitrate and phosphate loads from urban systems emitted to the environment, which increases to 11.2% and 19.5% in the case of no remediation scenario of projected defective sewer increases due to ageing effects. Our results provide a first harmonized quantification of potential leakage losses in urban wastewater systems at the nationwide scale and reveal the importance of rehabilitation planning of ageing sewer pipes in public and private sewer systems. The proposed model framework, which incorporates important factors for urban sewer managers, will allow further targeting the important data need for validating the approach at the regional and local scales in order to support better strategies for the long-term nutrient pollution control of large urban wastewater systems.

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The relevance of sewer deterioration modelling to support asset management strategies

Cited by 39 publications

References 23 publications

Sewer Condition Prediction and Analysis of Explanatory Factors

Sewer Condition Prediction and Analysis of Explanatory Factors

Optimizing SVM Model as Predicting Model for Sewer Pipes in the Two Main Cities in Colombia

Harmonized assessment of nutrient pollution from urban systems including losses from sewer exfiltration: a case study in Germany

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