Prioritizing inspection of sewer pipes based on self-cleansing criteria

Vanegas, Sergio; Montes, Carlos; Saldarriaga, Juan

doi:10.1080/1573062x.2022.2035408

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The main purpose is to identify the main strengths and weaknesses of the system's functioning, and to support the decision-making. Another research was carried out by Vanegas et al (2022), who used two performance indicators to evaluate the deterioration model of sediment deposit. The aim is to prioritise inspections of sewer pipes based on self-cleansing criteria.…”

Section: Introductionmentioning

confidence: 99%

Performance assessment of the operational management of urban drainage systems: a case study of Bejaia City, Algeria

Igroufa

Benzerra

Seghir

et al. 2022

Water Science and Technology

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Over the recent years, managers of the Algerian National Sanitation Office (ONA) have been worrying about the degraded state of their Urban Drainage Systems (UDS). This infrastructure failure is due essentially to the lack of funding and absence of adequate structured methodology for diagnosis and maintenance. As a result, ONA's managers found it very interesting to get a tool that allows them to assess their management of UDS operation. The aim of this paper is therefore to provide an assessment tool to managers for a good network operation. The adopted approach is participative and takes into account the specific local context. Six performance indicators grouped into two criteria, have been constructed to achieve this objective. These indicators have been chosen for their effectiveness, they are scaled to get their performance scores according to a scaling constructed here based on standards when available and to the ONA expert's recommendations. The developed tool is applied to the UDS of Bejaia City, in north-eastern of Algeria. The assessment and the analysis of the performance evolution of these indicators for the period 2017–2021 are carried out. The results highlighted successes that can be maintained as well as weaknesses that need to be improved.

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Section: Introductionmentioning

confidence: 99%

Performance assessment of the operational management of urban drainage systems: a case study of Bejaia City, Algeria

Igroufa

Benzerra

Seghir

et al. 2022

Water Science and Technology

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Sewer sediment deposition prediction using a two-stage machine learning solution

Ribalta Gené,

Béjar,

Mateu

et al. 2024

Journal of Hydroinformatics

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Sediment accumulation in the sewer is a source of cascading problems if left unattended and untreated, causing pipe failures, blockages, flooding, or odour problems. Good maintenance scheduling reduces dangerous incidents, but it also has financial and human costs. In this paper, we propose a predictive model to support the management of maintenance routines and reduce cost expenditure. The solution is based on an architecture composed of an autoencoder and a feedforward neural network that classifies the future sediment deposition. The autoencoder serves as a feature reduction component that receives the physical properties of a sewer section and reduces them into a smaller number of variables, which compress the most important information, reducing data uncertainty. Afterwards, the feedforward neural network receives this compressed information together with rain and maintenance data, using all of them to classify the sediment deposition in four thresholds: more than 5, 10, 15, and 20% sediment deposition. We use the architecture to train four different classification models, with the best score from the 5% threshold, being 82% accuracy, 70% precision, 76% specificity, and 88% sensitivity. By combining the classifications obtained with the four models, the solution delivers a final indicator that categorizes the deposited sediment into clearly defined ranges.

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Prioritizing inspection of sewer pipes based on self-cleansing criteria

Cited by 2 publications

References 32 publications

Performance assessment of the operational management of urban drainage systems: a case study of Bejaia City, Algeria

Performance assessment of the operational management of urban drainage systems: a case study of Bejaia City, Algeria

Sewer sediment deposition prediction using a two-stage machine learning solution

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