Park, Damdae Kim, Changsoo Kim, Kyeongsu 2024-10-26T07:00:21Z 2024-10-26T07:00:21Z 2024-10-25 2024-11 0256-1115 https://pubs.kist.re.kr/handle/201004/150867 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 insufficient data, we propose a systematic approach for deriving conservative probability distributions of pipeline defects. Based on the formal definition of conservative probability distributions, we present methods for modeling such distributions for pipeline defects, with the flexibility to adjust the degree of conservativeness. Furthermore, by incorporating Bayesian inference, we introduce a method for dynamic maintenance planning. The method enables effective 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-effective and safety-assured pipeline maintenance plans by quantitatively adjusting the degree of conservativeness, making it broadly applicable to various types of pipeline defects. English 한국화학공학회 Dynamic Maintenance of Underground Pipelines via a Systematic Approach for Conservative Estimation of Pipeline Defect Probability Density Under Data Scarcity Article 10.1007/s11814-024-00297-w 1 Korean Journal of Chemical Engineering, v.41, no.12, pp.3287 - 3297 Korean Journal of Chemical Engineering 41 12 3287 3297 N scie scopus kci 001328378100002 2-s2.0-85205807536 Chemistry, Multidisciplinary Engineering, Chemical Chemistry Engineering Article PITTING CORROSION DEPTH BURIED OIL MODEL Conservative probability distribution Reliability-based pipeline management Bayesian inference Dynamic maintenance plan Pipeline defect