Published August 24, 2022
| Version v1
Conference paper
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Machine learning to improve understanding of sewer pipe failures
Description
Paper presented in 10th International Conference on Sewer Processes and Networks
Highlights:
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Machine Learning shows strong promise for interpreting linked pipe asset and failure data
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Random Forests are able to predict if a pipe is at risk of causing a failure with high accuracy
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Through predicting failure probability, pipes at risk can be identified for proactive inspection
Notes
Files
SPN10_Kazemi-Machine_etal.pdf
Files
(1.1 MB)
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