Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains

Bakry, Ibrahim; Alzraiee, Hani; Masry, Mohamed S. El; Kaddoura, Khalid; Zayed, Tarek

doi:10.1061/(asce)cf.1943-5509.0000866

Cited by 20 publications

(5 citation statements)

References 11 publications

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“…The influence of material on the structural condition process of sewer pipes is well established in previous studies [3,6,58]. For example, although concrete pipes are significantly resistant to abrasion, they are vulnerable to the corrosive action of hydrogen sulfide [59]. The materials of the sewer pipes are shown in FIGURE 2b.…”

Section: ) Physical Factorsmentioning

confidence: 79%

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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Section: ) Physical Factorsmentioning

confidence: 79%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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“…To predict the future performance of trenchless rehabilitations, Ibrahim B et al [9] presented condition prediction models for the chemical grouting rehabilitation of pipelines and manholes in the city of Laval, Quebec, Canada. Bakry I et al [10] presented condition prediction models for CIPP rehabilitation of sewer mains, and the models can predict the structural and operational conditions of CIPP rehabilitation on the basis of basic input, such as pipe material, and rehabilitation type and date.…”

Section: Related Workmentioning

confidence: 99%

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Zhang

et al. 2022

Data Intelligence

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Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

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“…These are mathematical models, consisting of a collection of probability distributions to describe the patterns of variability. Bakry and al. (2016).…”

Section: R a F Tmentioning

confidence: 99%

Managing sewerage networks using both failure modes, effects and criticality analysis (FMECA) and analytic hierarchy process (AHP) methods

2021

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This paper proposes a methodology for managing complex sewerage networks based on the concomitant use of two performance evaluation methods namely (1): the Failure Modes, Effects and Criticality Analysis (FMECA) and (2) the Analytical Hierarchy Process (AHP). The FMECA is used to determine the risks of structural failures making it possible to establish a methodology for managing these failures. The AHP is used to check the relationship consistency between the performance indicators allowing the determination of the overall performance (OP). This proposed methodology was also utilized for the urban sewerage network of Oued-Kniss in the city of Algiers, Algeria, as part of the efforts engaged for sustainable and efficient management.

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Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains

Cited by 20 publications

References 11 publications

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Managing sewerage networks using both failure modes, effects and criticality analysis (FMECA) and analytic hierarchy process (AHP) methods

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