Reliability Assessment of a Fixed Jacket Platform by Monte Carlo Simulation Using Neural Network

Authors

1 MSc Student, Offshore Structural Engineering Department, Petroleum University of Technology

2 Assistant Professor, Offshore Structural Engineering Department, Petroleum University of Technology

Abstract

Fixed offshore structures are considered as an important structure in shallow water. Iranian oil and gas offshore structures which have been located in Persian Gulf are mostly fixed jacket. So reliability assessment of these kind of marine structures seems to be very important and is an essential part of offshore structure design. Mont Carlo is a powerful method which is used broadly for prediction of structure failure. The most advantage of this method is simplicity of implementation, the main limitation of this method is about the computational time due to the huge number of structural analyses. Incorporating the artificial neural network for the reduction of the sample size is used to get rid of MCS‘s time bottleneck. An MCS based method is introduced to take advantage of precision in optimization part. To solve the scaling problem of a large reliability analysis, an artificial neural network is employed. In this paper, an almost new constructed fixed jacket platform in the South Pars is selected and modeled using SACS software. In this regard, the nonlinear static pushover analysis is performed by application of nonlinear soil-pile interaction.  Analytical results show that the simultaneous use of these two techniques lead to more accurate and also faster reliability assessment. In MCS method probability of failure calculated using divide the number of failed sample by total samples which is concluded to the value of 9.4e05 for the test case structure in the current study. , and the reliability index is resulted to 3.73.

Keywords

References

1.Onoufriou, T. and V. Forbes, Developments in structural system reliability assessments of fixed steel offshore platforms. Reliability Engineering & System Safety, 2001. 71(2): p. 189-19
2. Nizamani, Z., Environmental load factors and system strength evaluation of offshore jacket platforms. Vol. 4. 2015: Springer.
3. Zaghloul, H.M. and B.F. Ronalds. The Use of Implicit Safety Factors in the Reassessment of Existing Platforms in the Arabian Gulf. in ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. 2004. American Society of Mechanical Engineers.
4. Fayazi, A., Reliability Based Assessment of Existing Fixed Offshore Platforms Located in the Persian Gulf. International Journal of Maritime Technology, 2015. 4: p. 37-50.
5. Chojaczyk, A., et al., Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety, 2015. 52: p. 78-89.
6. Rojas, J., A. El Hami, and D. Rade, Reliability analysis based on gradient and heuristic optimization techniques of composite laminates using element-free Galerkin method. International Journal for Simulation and Multidisciplinary Design Optimization, 2008. 2(2): p. 157-165.
7. Moghadas, R. and M. Fadaee, Reliability assessment of structures by Monte Carlo simulation and neural networks. Asian Journal of Civil Engineering (Building and Housing), 2012. 13(1): p. 79-88.
8. DNV-RP-2A, in in Recomanded practice for planning, Dessigning and constructing fixed offshore platform.WSD. 2000, DNV.
9. Melchers, R., Importance sampling in structural systems. Structural safety, 1989. 6(1): p. 3-10.
10. Bucher, C.G. and U. Bourgund, A fast and efficient response surface approach for structural reliability problems. Structural safety, 1990. 7(1): p. 57-66.
11. Novák, D., B. Teplý, and Z. Keršner. The role of Latin Hypercube Sampling method in reliability engineering. in Proc. of ICOSSAR. 1998.
12. Au, S.-K. and J.L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation. Probabilistic engineering mechanics, 2001. 16(4): p. 263-277.
13. Murotsu, Y., et al. Studies on assessment of structural reliability by response surface method and neural network. in Proceedings of the IFIP WG7. 5 Fifth Working Conference on Reliability and Optimization of Structural Systems VTakamatsu. 1993. North-Holland Publishing Co.
14. Shao, S. and Y. Murotsu, Structural reliability analysis using a neural network. JSME International Journal Series A Solid Mechanics and Material Engineering, 1997. 40(3): p. 242-246.
15. Papadrakakis, M. and N.D. Lagaros, Reliability-based structural optimization using neural networks and Monte Carlo simulation. Computer methods in applied mechanics and engineering, 2002. 191(32): p. 3491-3507.
16. Cardoso, J.B., et al., Structural reliability analysis using Monte Carlo simulation and neural networks. Advances in Engineering Software, 2008. 39(6): p. 505-513.
17. Harding, B., C. Tremblay, and D. Cousineau, Standard errors: A review and evaluation of standard error estimators using Monte Carlo simulations. Quant Methods Psychol, 2014. 10: p. 107-123.
18. Garrett, J.H., Editorial,where and why artificial neural networks are applicable in civil engineering. Journal of Computing in Civil Engineering, 1994. 8(2): p. 129-130.
19. Deng, J., et al., Structural reliability analysis for implicit performance functions using artificial neural network. Structural Safety, 2005. 27(1): p. 25-48.
20. McCulloch, W.S. and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943. 5(4): p. 115-133
21. Jeng, D., D. Cha, and M. Blumenstein. Application of neural network in civil engineering problems. in Proceedings of the international conference on advances in the internet, processing, systems and interdisciplinary research (IPSI-2003). 2003.
22. Document, S.P.P., SPD19B Jacket Inplace Analysis Calculation Note.
23. DNV-RP-C205, Environmental conditions and environmental loads. 2010, Norske Veritas.
24. Sorensen, R.M., Basic coastal engineering. Vol. 10. 2005: Springer Science & Business Media.
25. Environmental conditions and environmental loads, DNV-RP-C205. Det Norske Veritas: BÃerum, Norway, 2010.
International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)
Volume 2, Issue 3 - Serial Number 7
October 2017
Pages 57-64

Files

Share

How to cite

Statistics