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Open AccessFeature PaperReview

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

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Alan Plummer Associate, Inc., 1320 S University Dr # 300, Fort Worth, TX 76107, USA
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Center for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, Box 19308, Arlington, TX 76019, USA
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Department of Computer Science and Engineering, The University of Texas at Arlington, Box 19308, Arlington, TX 76019, USA
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EnTech Engineering P.C., 17 State Street, 36th Fl, New York, NY 10004, USA
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Author to whom correspondence should be addressed.
Infrastructures 2019, 4(4), 64; https://doi.org/10.3390/infrastructures4040064
Received: 4 September 2019 / Revised: 1 October 2019 / Accepted: 18 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Water Infrastructure Asset Management)
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. View Full-Text
Keywords: sewer condition prediction; sewer pipe prioritization; deterioration models; pipe condition assessment; asset management sewer condition prediction; sewer pipe prioritization; deterioration models; pipe condition assessment; asset management
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MDPI and ACS Style

Malek Mohammadi, M.; Najafi, M.; Kaushal, V.; Serajiantehrani, R.; Salehabadi, N.; Ashoori, T. Sewer Pipes Condition Prediction Models: A State-of-the-Art Review. Infrastructures 2019, 4, 64.

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