Improving Subsurface Asset Failure Predictions for Utility Operators: A Unique Case Study on Cable and Pipe Failures Resulting from Excavation Work
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 2
Abstract
Utility operators must rely on predictive analyses regarding the availability of their subsurface assets, which highly depend on damage by increasing amounts of excavation work. However, straightforward use of standard statistical techniques, such as logistic regression or Bayesian logistic regression, does not allow for accurate predictions of these rare events. Therefore, in this paper, alternative approaches are investigated. These approaches involve weighting the likelihood as well as over- and undersampling the data. It was found that these data methods could substantially improve the accuracy of predicting rare failure events. More specifically, an application based on the real data of a Dutch water utility operator showed that undersampling and weighting improved the balanced accuracy, varying between 0.61 and 0.66, whereas the proposed methods resulted in failure predictions on between 38% and 58% of the validation data set. Hence, the proposed methods will enable utility operators to arrive at more accurate forecasts, enhancing their asset operation decision-making.
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Data Availability Statement
All data and models are proprietary or confidential in nature. All statistical code used during this study is available from the corresponding author upon request.
Acknowledgments
The authors would like to thank Evides Waterbedrijf for providing the data set and its contribution during preparation of the data set used in this study. The contribution of the municipality of Rotterdam that provided the data (Rotterdam3D) is deeply appreciated.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 2 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jan 9, 2019
Accepted: Dec 12, 2019
Published online: Mar 24, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 24, 2020
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