Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks

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

doi:10.1061/(asce)cf.1943-5509.0000893

Cited by 19 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ariaratnam et al [2] analyzed the impact of physical factors, and the results indicated that a larger diameter has a positive effect on the structural condition of sewer pipes. Bakry et al [24] also found the same implication: pipes with larger diameters are less influenced by deterioration in terms of structural and operational conditions. Conversely, several researchers have stated that larger pipes are more likely to fail due to the larger surface, heavier weight and bulk [10,25].…”

Section: Physical Factorsmentioning

confidence: 72%

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

et al. 2021

View full text Add to dashboard Cite

The escalating number of aging sewer pipes necessitates an infrastructure asset management approach to achieve an efficient budget allocation for maintenance. This study suggests a risk-based prioritization framework for sewer pipe inspection considering the predicted condition of sewer pipes and the criticality of the economic, social and environmental impacts associated with them. The results from both models can be used to evaluate the risk of sewer pipes by classification into risk groups. A risk matrix is used for the classification, and it divides the sewer pipes into five risk groups. The results of this study show an improvement in the accuracy of finding sewer pipes in a bad condition using this framework. The condition prediction model can successfully find sewer pipes with a bad condition with over 70% precision. High-risk sewer pipes are highlighted using the differences in the environmental features as well as in the physical features associated with other sewer pipes. Additionally, through the combination of both the condition and criticality of sewer pipes, the framework systemically prioritizes needed maintenance for sewer pipes with a very bad condition. This prioritization framework is expected to help the process of deciding which sewer pipes should be prioritized within a constrained budget.

show abstract

Section: Physical Factorsmentioning

confidence: 72%

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Intelligent decision provides the basis. 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

View full text Add to dashboard Cite

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.

show abstract

“…In addition, the accuracy of the models was determined by calculating mean absolute error and root mean square error. Linear deterioration curves were developed in this paper by examining the effect of increasing the age while changing the dependent variables [25,26].…”

Section: Previous Studiesmentioning

confidence: 99%

Sewer Pipes Condition Prediction Models: A State-of-the-Art Review

Mohammadi¹,

Najafi

Kaushal

et al. 2019

Infrastructures

View full text Add to dashboard Cite

Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.

show abstract

Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks

Cited by 19 publications

References 12 publications

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

Risk-Based Prioritization of Sewer Pipe Inspection from Infrastructure Asset Management Perspective

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

Sewer Pipes Condition Prediction Models: A State-of-the-Art Review

Contact Info

Product

Resources

About