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Predicting Bending Moments with Machine Learning

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Geometric Science of Information (GSI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11712))

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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.

This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme.

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Author information

Authors and Affiliations

  1. NTNU, Trondheim, Norway

    Elena Celledoni, Halvor S. Gustad, Nikita Kopylov & Henrik S. Sundklakk

Authors
  1. Elena Celledoni
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  2. Halvor S. Gustad
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  3. Nikita Kopylov
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  4. Henrik S. Sundklakk
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Corresponding author

Correspondence to Nikita Kopylov .

Editor information

Editors and Affiliations

  1. Sony Computer Science Laboratories, Inc., Tokyo, Japan

    Frank Nielsen

  2. Thales, Limours, France

    Frédéric Barbaresco

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Celledoni, E., Gustad, H.S., Kopylov, N., Sundklakk, H.S. (2019). Predicting Bending Moments with Machine Learning. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2019. Lecture Notes in Computer Science(), vol 11712. Springer, Cham. https://doi.org/10.1007/978-3-030-26980-7_19

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  • DOI: https://doi.org/10.1007/978-3-030-26980-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26979-1

  • Online ISBN: 978-3-030-26980-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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