Abstract
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.
Highlights
U.S infrastructure plays a critical role in urban communities, providing for the safe and efficient conveyance of water, sewer, gas, and other lifelines to protect human health and the environment
The linear regression model is too simplistic to display the probabilistic nature of pipe deterioration [17,27,28,29]
The result is obtained from the relationship between mean values of dependent and independent variables and sometimes they are not enough strong for models with multiple input variables
Summary
U.S infrastructure plays a critical role in urban communities, providing for the safe and efficient conveyance of water, sewer, gas, and other lifelines to protect human health and the environment. Ana and Bauwens (2010) suggested that the best way to forecast pipe failure and deterioration time is development of probability-based condition prediction models based on actual inspection database [6]. The aging of sewer pipes increases the failure rates and can result in social, environmental, and economic impacts, such as water quality issues including chemical or biological contaminations, which may cause illness and extensive repair costs [8]. The current trend is to maintain and manage pipe systems before failure time. Asset management programs can develop various strategies to help utility companies and municipalities to understand the timing and associated costs of maintenance, rehabilitation, or replacement of the pipes [9]. This paper illustrates and studies the most common statistical models used in predicting deterioration and condition states of sewer pipes
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