A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/4/952 |
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| author | Comfort Salihu Saeed Reza Mohandes Ahmed Farouk Kineber M. Reza Hosseini Faris Elghaish Tarek Zayed |
| author_facet | Comfort Salihu Saeed Reza Mohandes Ahmed Farouk Kineber M. Reza Hosseini Faris Elghaish Tarek Zayed |
| author_sort | Comfort Salihu |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-11T05:10:51Z |
| format | Article |
| id | doaj.art-3496772d4ccf48188f601a41c559b254 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T05:10:51Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-3496772d4ccf48188f601a41c559b2542023-11-17T18:35:25ZengMDPI AGBuildings2075-53092023-04-0113495210.3390/buildings13040952A Deterioration Model for Sewer Pipes Using CCTV and Artificial IntelligenceComfort Salihu0Saeed Reza Mohandes1Ahmed Farouk Kineber2M. Reza Hosseini3Faris Elghaish4Tarek Zayed5Department of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, ChinaDepartment of Mechanical, Aerospace and Civil Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UKDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaSchool of Architecture and Built Environment, Deakin University, Geelong 3220, AustraliaSchool of Natural and Built Environment, Queen’s University Belfast, Belfast BT7 1NN, UKDepartment of Building and Real Estate (BRE), Faculty of Construction and Environment (FCE), The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, ChinaSewer 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 R<sup>2</sup> 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.https://www.mdpi.com/2075-5309/13/4/952machine learningdeterioration modelsmaintenanceartificial intelligencerobot-based inspection techniques |
| spellingShingle | Comfort Salihu Saeed Reza Mohandes Ahmed Farouk Kineber M. Reza Hosseini Faris Elghaish Tarek Zayed A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence Buildings machine learning deterioration models maintenance artificial intelligence robot-based inspection techniques |
| title | A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence |
| title_full | A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence |
| title_fullStr | A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence |
| title_full_unstemmed | A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence |
| title_short | A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence |
| title_sort | deterioration model for sewer pipes using cctv and artificial intelligence |
| topic | machine learning deterioration models maintenance artificial intelligence robot-based inspection techniques |
| url | https://www.mdpi.com/2075-5309/13/4/952 |
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