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Machine Learning for Pipe Condition Assessments
Journal American Water Works Association ( IF 0.7 ) Pub Date : 2020-05-05 , DOI: 10.1002/awwa.1501
James C. Fitchett , Kosmas Karadimitriou , Zella West , David M. Hughes

Utilities replace water mains by responding to failures or proactively choosing pipes likely to fail. Machine learning can find fragile pipes more accurately than using age or historical breaks as indicators.

中文翻译:

用于管道状况评估的机器学习

公用事业通过响应故障或主动选择可能发生故障的管道来代替自来水。与使用年龄或历史中断作为指标相比,机器学习可以更准确地找到易碎的管道。
更新日期:2020-05-05
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