Development of a Framework for Design and Installation of a New Lining Material for Water Pipeline Renewal

Mohammadi, Mohammadrza Malek; Aaron, Homing Ni; Najafi, Mohammad; Korky, Seyedmohammadsadegh Jalalediny; Serajiantehrani, Ramtin

doi:10.1061/9780784482483.014

Cited by 3 publications

(1 citation statement)

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“…The pipe group models, such as Markov, with the ability of forecasting behavior of pipe networks, are useful to allocate budget for maintaining and rehabilitating groups of pipes. Meanwhile, pipe level models such as logistic regression are more appropriate for assessing the condition of individual pipes and prioritizing each pipe segment [6,51]. Additionally, probabilistic-based models such as Markov and logistic regression are useful to determine and analyze the risk of pipe failure in the network.…”

Section: Discussionmentioning

confidence: 99%

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

Mohammadi¹,

Najafi

Kaushal

et al. 2019

Infrastructures

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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.

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Section: Discussionmentioning

confidence: 99%