A deterioration model for sewer pipes using cctv and artificial intelligence

Comfort Salihu, Saeed Reza Mohandes, Ahmed Farouk Kineber, M. Reza Hosseini, Faris Elghaish, Tarek Zayed

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
140 Downloads (Pure)


Sewer pipeline failures pose significant threats to the environment and public health. To tackle these repercussions, many deterioration models have been developed to predict the conditions of sewer pipes, most of which are based on CCTV inspection reports. However, these reports are prone to errors due to their subjective nature and human involvement. More importantly, there are insufficient data to develop prudent deterioration models. To address these shortcomings, this paper aims to develop a CCTV-based deterioration model for sewer pipes using Artificial Intelligence (AI). The AI-based model relies on the integration of an unsupervised, multilinear regression technique and Weibull analysis. Findings derived from the Weibull deterioration curve indicate that the useful service life for concrete and vitrified clay pipes are 79 years and 48 years, respectively. The regression models show that the R2 value for vitrified clay sewer pipes, concrete sewer pipes, and ductile iron sewer pipes are 71.18%, 71.47%, and 81.51%, respectively, and 73.69% for concrete stormwater pipes. To illustrate the impact of various factors on sewer pipes, sensitivity analyses under different scenarios are conducted. These analyses indicate that pipe diameter has a significant influence on sewer pipe deterioration, with little impact on stormwater pipes. These findings would guide decision makers in identifying critical pipes and taking necessary precautionary measures. Further, this provides a sound basis for prioritizing maintenance actions, which would pave the way for designing sustainable urban drainage systems for cities.
Original languageEnglish
Article number952
Number of pages20
Issue number4
Early online date03 Apr 2023
Publication statusEarly online date - 03 Apr 2023


  • Building and Construction
  • Civil and Structural Engineering
  • Architecture


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