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Predicting Bending Moments with Machine Learning

  • Elena Celledoni
  • Halvor S. Gustad
  • Nikita KopylovEmail author
  • Henrik S. Sundklakk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11712)

Abstract

We investigate the possibility of predicting the bending moment of slender structures based on a limited number of deflection measurements. These predictions can help to estimate the wear and tear of the structures. We compare linear regression and a recurrent neural network on numerically simulated Euler–Bernoulli beam and drilling riser.

Keywords

Euler–Bernoulli beam Drilling riser Slender structures Material fatigue Machine learning 

References

  1. 1.
    Authén, K.: Learning from riser analyses and predicting results with artificial neural networks. In: Volume 3B: Structures, Safety and Reliability. ASME, V03BT02A056, June 2017.  https://doi.org/10.1115/OMAE2017-61775
  2. 2.
    Burkov, A.: The Hundred-Page Machine Learning Book, Quebec (2019) Google Scholar
  3. 3.
    Chang, B.: et al.: AntisymmetricRNN: a dynamical system view on recurrent neural networks. In: International Conference on Learning Representations (2019)Google Scholar
  4. 4.
    Chollet, F.: Deep Learning with Python, vol. 28, p. 384. Manning Publications, New York (2017)Google Scholar
  5. 5.
    Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. Inverse Prob. 34(1), 014004 (2017).  https://doi.org/10.1088/1361-6420/aa9a90MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    He, W., et al.: Dynamics and Control of Mechanical Systems in Off Shore Engineering. Springer-Verlag, London (2014).  https://doi.org/10.1007/978-1-4471-5337-5CrossRefGoogle Scholar
  7. 7.
    Hochbruck, M., Ostermann, A.: Exponential integrators. Acta Numerica 19, 209–286 (2010).  https://doi.org/10.1017/S0962492910000048MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Liu, K., et al.: Nonlinear dynamic analysis and fatigue damage assessment for a deepwater test string subjected to random loads. Petrol. Sci. 13(1), 126–134 (2016).  https://doi.org/10.1007/s12182-015-0063-4CrossRefGoogle Scholar
  9. 9.
    Liu, X., et al.: Analysis on the operation fatigue of deepwater drilling riser system. Open Petrol. Eng. J. 9(1), 279–287 (2016).  https://doi.org/10.2174/1874834101609010279CrossRefGoogle Scholar
  10. 10.
    Meier, C., Popp, A., Wall, W.A.: Geometrically exact finite element formulations for slender beams: Kirchhoff–Love theory versus Simo–Reissner theory. Arch. Comput. Methods Eng. 26(1), 163–243 (2019).  https://doi.org/10.1007/s11831-017-9232-5MathSciNetCrossRefGoogle Scholar
  11. 11.
    Morison, J.R., et al.: The force exerted by surface waves on piles. Petrol. Trans., AIME 189(4), 149–154 (1950)Google Scholar
  12. 12.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Raissi, M., et al.: Deep learning of vortex-induced vibrations. J. Fluid Mech. 861, 119–137 (2019).  https://doi.org/10.1017/jfm.2018.872MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Sparks, C.: Fundamentals of Marine Riser Mechanics. PennWell Corporation, Tulsa (2007)Google Scholar
  15. 15.
    Vikebø, F., et al.: Wave height variations in the North Sea and on the Norwegian Continental Shelf, 1881–1999. Cont. Shelf Res. 23(3–4), 251–263 (2003).  https://doi.org/10.1016/s0278-4343(02)00210-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NTNUTrondheimNorway

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