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Considering the importance of offshore structures, one has to recognize that there are other intrinsic uncertainties such as material properties, analysis methods, design procedures etc, which are addressed rationally. A detailed knowledge of reliability of offshore structures using probabilistic tools becomes need of the hour for both industry and academia. Offshore activities, on one hand, lead to increase in societal wealth, and, on the other hand, make society vulnerable to risks. An offshore engineer is usually accountable for the decisions that he takes. A hallmark of professionalism is to quantify the risks and benefits involved. The present course aims to introduce the basics of the structural reliability analysis procedures. The Registrants would benefit from the course by learning the basics of reliability-based design and principles underlying code calibration, which would provide the groundwork to embark upon research in this field. Key focus will be on safety and reliability issues of offshore facilities during analysis and design, inspection and planning.  




Concepts of probability 
Sampling statistics
Types of uncertainties
Modeling random variables like loads, material properties etc
Introduction to classical reliability theories
Error estimation


Levels of reliability
Reliability estimates
FOSM, AFOSM and application problems
Codes of practice of safety check
Reliability bounds of structural systems
Treatment of geometric variables
Probabilistic methods of code calibrations


Application to offshore structures
Stochastic process
Gaussian process
Risk assessment
Hazard identification
Risk modeling and Risk picture
Probabilistic risk assessment

UG/PG/Ph.D of all engg branches and PG of applied sciences; Diploma students can also register

a) Text books:

  1. Almond R.G. An extended example for testing graphical belief, Technical Report No. 6.1992.
  2. Chakrabarti, S.K. 1990. Non-linear Method in Offshore Engineering, Elsevier Science Publisher, The Netherlands.
  3. Chakrabarti, S.K. 1994. Offshore Structure Modeling: World Scientific, Singapore.
  4. Chandrasekaran, S. and Bhattacharyya, S.K. 2011. Analysis and Design of Offshore Structures. HRD Center for Offshore and Plant Engineering (HOPE), Changwon National University, Republic of Korea, pp. 285.
  5. Cowell RG, Dawid AP, Lauritzen SL, Spiegelhalter DJ. Probabilistic networks and expert systems. New York: Springer; 1999.
  6. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. London: Chapman & Hall; 1995. p. 1-526.
  7. Halder, A. and Mahaderan, S.,"First order and Second order Reliability Method" Probabilistic Structural Mechanics Hand Book, Edited by C. (Raj) Sundararajan, Chapman and Hall, PP. 27-52, 1995.
  8. Jensen FV. Bayesian networks and decision graphs. New York: Springer; 2001.
  9. Pearl J. Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann; 1988.
  10. Srinivasan Chandrasekaran. 2014. Advanced Theory on Offshore Plant FEED Engineering, Changwon National University Press, Republic of South Korea, pp. 237. ISBN:978-89-969792-8-9
  11. Srinivasan Chandrasekaran. 2015. Advanced Marine structures, CRC Press, Florida, ISBN 9781498739689
  12. Srinivasan Chandrasekaran. 2015. Dynamic analysis and design of ocean structures. Springer. ISBN: 978-81-322-2276-7.
  13. Srinivasan Chandrasekaran. 2016a. Offshore structural engineering: Reliability and Risk Assessment. CRC Press, Florida, ISBN:978-14-987-6519-0

Research articles

  1. Arnasaki S, Takagi Y, Mizuno 0, Kikuno T. A Bayesian belief network for assessing the likelihood of fault content. In: Proceedings of the 14th international symposium on software reliability engineering; 2003. p. 215-26.
  2. Barlow RE. Using influence diagrams. Accelerated Life Testing and Experts' Opinions in Reliability 1988:145-50.
  3. Bobbio A. Portinale L. Minichino M. Ciancamerla E. Improving the analysis of dependable systems by mapping fault trees into
  4. Bayesian networks. Reliab Eng Syst Saf 2001:71(3):249-60.
  5. Boudali H, Dugan JB. A continuous-time Bayesian network reliability modeling, and analysis framework. IEEE Trans Reliab 2006:55(1):86-97. Box, G. E. P., and Tiao, G. C.,"Bayesian Inference in Statical Analysis", Addison-Wesley, Reading, MA 1973.
  6. Breitung, K.,"Asymptotic Approximation for Multi-normal Integrals", Journal of Engineering Mechanics Division, ASCE, 110(3), PP. 357-366, 1984
  7. Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992:9(4):309-47.
  8. Gopal C, Kuolung H, Nader A. A new approach to system reliability. IEEE Trans Reliab 2001:50(1):75-84.
  9. Cornel, C. A.,"A Probability Based Structural Code", Journal of the American Concrete Institute, 66(12), PP. 974-085, 1969.
  10. Coyle T. Arno RG. Hale PS. Application of the minimal cut set reliability analysis methodology to the gold book standard network. In: Proceedings of the industrial and commercial power systems technical conference; 2002. p. 82-93.
  11. Dahll G. Gran BA. The use of Bayesian belief nets in safety assessment of software based systems. Special Issues Int J Intelligent Inf Syst 2000;24(2): 205-29.
  12. Der Kiureghian, Lin, H. Z., and Hwang, S. F.,"Second order Reliability Approximation", Journal of Engineering Mechanics Division, ASCE, 113(8), PP. 1208-1225, 1987.
  13. Fenton N. Krause P. Neil M. Software measurement: uncertainty and causal modeling. IEEE Software 2002;10(4):116-22.
  14. Fiessler, B., Neumann, H. J., and Rackwitz, R.,"Quadratic Limit States in Structural Reliability", Journal 0of Engineering Mechanics Division, ASCE, 105(4), PP. 661-676, 1979.
  15. Ghokale S, Lyu M, Trivedi K. Reliability simulation of component based software systems. In: Proceedings of the international symposium on software reliability engineering (ISSRE'98); 1998.
  16. Ghokale S, Wong E, Trivedi K, Horgan JR. An analytical approach to architecture based software reliability prediction. In: Proceedings of the symposium on application specific systems and software engineering technology (ASSET '98). TX: Dallas; 1998.
  17. Gran BA, Helminen A. A Bayesian belief network for reliability assessment. Safecornp 2001 20012187:35-45.
  18. Gran BA. Dahill G. Eisinger S. Lund EJ, Norstrom JG. Strocka P. Ystanes BJ. Estimating dependability of programmable systems using Bbns. In: Proceed-ings of the Safecornp 2000. Berlin: Springer; 2000. p. 309-20.
  19. Helminen A, Pulld
  20. Helminen A. Reliability estimation of software-based digital systems using Bayesian networks. Technical Report. Helsinki University of Technology Espoo, 2000. p. 1-50.
  21. Herald T, Ramirez-Marquez JE. System element obsolescence replacement optimization via life cycle cost forecasting. NJ:
  22. Hugin Expert. (2007), Aalborg, Denmark (2008).
  23. Krishnarnurthy S. Mathur AP. On the estimation of reliability of a software system using reliabilities of its components. In: Proceedings of the in the international symposium on software reliability engineering ISSRE '97). NM: Albuquerque; 1997. p. 146.
  24. Lagnseth H, Portinale L. Bayesian networks in reliability. Tech Rep 2005.
  25. Littlewood B. Popov P. Strigini L. Assessment of the reliability of fault-tolerant software: a bayesian approach. In: Proceedings of the 19th international conference on computer safety, reliability and security (SAFECOMP 2000). Berlin: Springer; 2000
  26. Pant K, Brandt S. Null convention logic, a complete and consistent logic for asynchronous digital circuit synthesis. In: Proceedings of the international conference on application specific systems, architectures, and processors (ASAP '96); 1996. p. 261-73.
  27. Rackwitz, R., and Fiessler, B., Note on Discrete Safety Checking When Using Non-Normal Stochastic Models for Basic Variables. Loads Project Working Session. Cambridge, Massachusetts: Massachusetts Institute of Technology. 1976.
  28. Serene-Safety and Risk Evaluation Using Bayesian Nets, (2006), (2008).
  29. Shinozuka, M.,"Basic Analysis of Structural Safety", Journal of the Structural Division, ASCE, 109(3), PP. 721-740, 1983.
  30. Sigurdsson JH, Walls LA, Quigley JL. Bayesian belief nets for managing expert judgment and modeling reliability. Qual Reliab Eng Int 2001;17:181-90.
  31. Spiegelhalter D. Thomas A. Best N. Gilks W. Bugs 0.5 Bayesian inference using Gibbs sampling manual (Version Ii). MRC Biostatistic Unit 1996;1:1-59.
  32. Tvedt, L.,"Distribution of Quadratic forms in Normal Space-Application to Structural Reliability", Journal of the Engineering Mechanics Division, ASCE, 116(6), PP. 1183-1197, 1990.

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