Development of a Model to Prioritize Inspection and Condition Assessment of Gravity Sanitary Sewer Systems
Publication: Pipelines 2022
ABSTRACT
Municipal wastewater collection systems deteriorate over time demanding utility owners to involve in continuous revisions and development of asset management frameworks. Inspection and condition assessment of pipelines play a vital role in the successful operation and maintenance of systems. In the United States, closed-circuit television (CCTV) is the commonly used device for inspecting the inner environment of sanitary sewer pipes. Inspection of every individual sanitary sewer pipe segment is not feasible for any municipality owing to their large inventory of pipes and incurred cost. However, sanitary sewer pipe segments in need of repair or maintenance activity can be prioritized in advance for inspection based on their historical performance. Therefore, a condition prediction model based on various machine learning techniques, such as logistic regression (LR), k-nearest neighbors (k-NN), and random forests (RF), is developed. The developed model could forecast the condition of uninspected sanitary sewer pipe. With an area under the curve (AUC) value of 0.86, the random forests model performed better than both LR and k-NN models. The developed models can be utilized by utility owners and municipal asset managers to make more informed decisions on future inspections of sewer pipelines.
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Pipelines 2022
Pages: 1 - 9
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Published online: Jul 28, 2022
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Cited by
- Karthikeyan Loganathan, Mohammad Najafi, Praveen Kumar Maduri, Kawalpreet Kaur, Condition Prediction of Sanitary Sewer Pipe Data Set with Imbalanced Classification, Pipelines 2023, 10.1061/9780784485033.019, (170-180), (2023).
- Kawalpreet Kaur, Sanaz Ghalambor, Mohammad Najafi, Loganathan Karthikeyan, Salar Shirkhanloo, Short-Term Hole Spanning Testing and Evaluation for Spray Applied Pipe Lining, Pipelines 2023, 10.1061/9780784485026.020, (181-190), (2023).