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- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received September 3, 2024
Accepted September 25, 2024
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Latest issues
Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity
Abstract
The scarcity of the defect data may lead to the underestimation of defects, resulting in maintenance plans with inspection
intervals that may not guarantee timely repairs. To address the low reliability of defect distribution models developed from
insuffi cient data, we propose a systematic approach for deriving conservative probability distributions of pipeline defects.
Based on the formal defi nition of conservative probability distributions, we present methods for modeling such distributions
for pipeline defects, with the fl exibility to adjust the degree of conservativeness. Furthermore, by incorporating Bayesian
inference, we introduce a method for dynamic maintenance planning. The method enables eff ective utilization of the limited
defect data samples obtained during pipeline inspection to assess overall pipeline conditions and dynamically determine
subsequent maintenance intervals. The simulation results demonstrate that the proposed method can achieve cost-eff ective
and safety-assured pipeline maintenance plans by quantitatively adjusting the degree of conservativeness, making it broadly
applicable to various types of pipeline defects.
