Predicting Water Pipe Failures Using Deep Learning Algorithms
Publication: Journal of Infrastructure Systems
Volume 29, Issue 3
Abstract
With the increase in the operation risks of water distribution networks (WDNs), the prediction of pipe failures is of great significance in developing efficient maintenance strategies. This study used a residual network (ResNet), a newly proposed deep learning (DL) algorithm, to predict pipe failure, and its effectiveness was compared with that of a classic convolution neural network (CNN) algorithm. Network structure of ResNet used in the classification of one-dimensional pipe vectors was built. The synthetic minority oversampling technique (SMOTE) was used to improve the prediction accuracy because of the imbalanced pipe database provided by the local water sector. The analysis of a real WDN in China showed that ResNet performed better than CNN in terms of recall rate and area under the receiver operating characteristic curve with reasonable time costs. The maintenance rate was defined and discussed to measure the efficiency of maintenance activities. More than half of the failures can be prevented by maintaining less than 10% of the pipes based on the proposed ResNet algorithm. In addition, the Shapley Additive exPlanations (SHAP) method was used to interpret the DL model. The SHAP method evaluated the impact of different features on pipe failure, and the pipe length and diameter were proved to be two influential features.
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Data Availability Statement
All data generated or used during the study are available from the corresponding author upon reasonable request.
Acknowledgments
The support from National Key R & D Program of China (2022YFC3801000), the Ministry of Science and Technology of China (SLDRCE19-B-24), the Shanghai Municipal Commission of Housing and Urban-Rural Development (2021-001-006), and the Shanghai Chengtou Group Corporation (CTKY-PTRC-2021-005-002) is greatly appreciated.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 29 • Issue 3 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 12, 2022
Accepted: May 5, 2023
Published online: Jul 4, 2023
Published in print: Sep 1, 2023
Discussion open until: Dec 4, 2023
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Architectural engineering
- Artificial intelligence and machine learning
- Building management
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Failure analysis
- Infrastructure
- Maintenance and operation
- Mathematics
- Neural networks
- Pipe failures
- Pipe networks
- Pipeline management
- Pipeline systems
- Pipelines
- Pipes
- Water and water resources
- Water management
- Water pipelines
- Water supply
- Water supply systems
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