TY - GEN
T1 - Neutrosophic Risk-Based Inspection on Crude Oil Pipeline
AU - Butsaina, Nafisa Aqila
AU - Mukhlash, Imam
AU - Wahono, Tri
AU - Aini, Qonita Qurratu
AU - Putri, Endah R.M.
AU - Purniawan, Agung
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Effective risk assessment and prioritization are crucial for maintaining the integrity of pipeline systems in the oil and gas industry. Pipeline system failure can result in severe consequences. The risk-based inspection (RBI) is a commonly used method for assessing risks with API 581 and API 580 as the standards. Qualitative RBI relies on expert judgment to estimate the consequence of failure (CoF) and the probability of failure (PoF), and its categorical risk classification introduces subjectivity and uncertainty. Many studies have explored fuzzy logic, including classical and intuitionistic fuzzy sets, to reduce uncertainty in expert judgments. In this work, we propose a linguistic-based approach that integrates neutrosophic sets with the risk-based inspection method within the API framework. Expert judgments are gathered in linguistic terms and converted into triangular neutrosophic numbers. These values are transformed into crisp numbers using the de-neutrosophication concept which are then used to calculate risk levels, rank priorities, and present the results in a classified risk matrix. The proposed approach is compared with the qualitative RBI method to examine the ranking consistency, differences, and potential advantages in handling uncertainty. This method offers a new alternative framework for evaluating pipeline thinning risks and refining inspection prioritization.
AB - Effective risk assessment and prioritization are crucial for maintaining the integrity of pipeline systems in the oil and gas industry. Pipeline system failure can result in severe consequences. The risk-based inspection (RBI) is a commonly used method for assessing risks with API 581 and API 580 as the standards. Qualitative RBI relies on expert judgment to estimate the consequence of failure (CoF) and the probability of failure (PoF), and its categorical risk classification introduces subjectivity and uncertainty. Many studies have explored fuzzy logic, including classical and intuitionistic fuzzy sets, to reduce uncertainty in expert judgments. In this work, we propose a linguistic-based approach that integrates neutrosophic sets with the risk-based inspection method within the API framework. Expert judgments are gathered in linguistic terms and converted into triangular neutrosophic numbers. These values are transformed into crisp numbers using the de-neutrosophication concept which are then used to calculate risk levels, rank priorities, and present the results in a classified risk matrix. The proposed approach is compared with the qualitative RBI method to examine the ranking consistency, differences, and potential advantages in handling uncertainty. This method offers a new alternative framework for evaluating pipeline thinning risks and refining inspection prioritization.
KW - API
KW - Consequence of Failure
KW - Neutrosophic sets
KW - Probability of Failure
KW - Risk-based inspection (RBI)
UR - https://www.scopus.com/pages/publications/105013049401
U2 - 10.1007/978-3-031-97992-7_81
DO - 10.1007/978-3-031-97992-7_81
M3 - Conference contribution
AN - SCOPUS:105013049401
SN - 9783031979910
T3 - Lecture Notes in Networks and Systems
SP - 753
EP - 760
BT - Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference
A2 - Kahraman, Cengiz
A2 - Cebi, Selcuk
A2 - Oztaysi, Basar
A2 - Cevik Onar, Sezi
A2 - Tolga, Cagri
A2 - Ucal Sari, Irem
A2 - Otay, Irem
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025
Y2 - 29 July 2025 through 31 July 2025
ER -