Predicting Bending Moments with Machine Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11712)


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.


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


  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.
  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). Scholar
  6. 6.
    He, W., et al.: Dynamics and Control of Mechanical Systems in Off Shore Engineering. Springer-Verlag, London (2014). Scholar
  7. 7.
    Hochbruck, M., Ostermann, A.: Exponential integrators. Acta Numerica 19, 209–286 (2010). 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). 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). 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). 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). 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). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NTNUTrondheimNorway

Personalised recommendations