Condition Prediction of Sanitary Sewer Pipes

Mohammadi, Mohammadrza Malek; Najafi, Mohammad; Tabesh, Amir; Riley, J.A.; Gruber, Jessica

doi:10.1061/9780784482490.013

Cited by 25 publications

(16 citation statements)

References 5 publications

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“…ROC curve illustrates the exchange between true positive to false positive rates. In ROC curve, the X-axis illustrates a false positive rate (specificity) and the Y-axis presents a true positive rate (sensitivity) [50].…”

Section: Discussionmentioning

confidence: 99%

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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%

“…The performance of the model was validated by confusion matrix. The result indicated that pipe age, material, diameter, length, and watertable are the significant factors affecting deterioration of sewer pipes [46].…”

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

show abstract

“…This factor represents the degree of deterioration since deterioration begins right after the installation of sewer pipes [20]. However, newly installed pipes can also lead to failure because of human errors or damage to the pipes during construction [21]. Pipes that do not fail at this early stage go through a plateau, and such pipes are usually damaged by an external interference, such as extremely heavy loading.…”

Section: Physical Factorsmentioning

confidence: 99%

“…They stated that the vibration caused by a large traffic volume can deteriorate the pipe condition. Groundwater is also an important factor since it may flow into the pipes when sewer pipes are installed below the groundwater level, thus increasing structural defects [21]. Additionally, the infiltration of groundwater washes the soil surrounding the sewer pipes, meaning that the soil support is weakened [18].…”

Section: Environmental Factorsmentioning

confidence: 99%

“…The purpose of prioritization is to classify the sewer pipes considering the results of the condition prediction model and the criticality assessment model together. This study adopted the risk matrix method, which is widely used in decision making, enabling the consideration of both probability and the resultant impact [21]. To form the risk matrix, it was necessary to set the cut-off values for the condition and the criticality.…”

Section: Prioritizationmentioning

confidence: 99%

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

et al. 2021

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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.

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