Affiliation:
1. Construction Engineering & Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
2. Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Abstract
The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference41 articles.
1. Olson D.L. , Wu D.D. , Enterprise risk management models, Heilderbeg: Springer, Berling Heilderbeg: Springer, (2010).
2. Managing information flow and design processes to reduce design risks in offsite construction projects;Sutrisna;Engineering, Construction and Architectural Management,2019
3. Risk assessment and risk management: review of recent advances on their foundation;Aven;European Journal of Operational Research,2016
4. Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction;Zhou;Safety Science,2016
5. Foundations for industrial machines and earthquake effects;Bhatia;ISET Journal of Earthquake Technology,2008
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