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Predicting Bending Moments with Machine Learning

Conference paper
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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 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NTNUTrondheimNorway

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