Predicting nonlinear seismic response of structural braces using machine learning

Elif Ecem Bas, Denis Aslangil, Mohamed Aly Moustafa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to improve the modeling capabilities by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can represent real physics? (iii) How can our findings be extended to the different engineering problems that are driven by similar nonlinear dynamics? To answer these questions, the presented methods are validated by using experimental brace data. The paper shows that after proper data preparation, the long-short term memory (LSTM) method is highly capable of capturing the nonlinear behavior of braces. Additionally, the effects of tuning the hyperparameters on the models, such as layer numbers, neuron numbers, and the activation functions, are presented. Finally, the ability to learn nonlinear dynamics by using deep neural network algorithms and their advantages are briefly discussed.

Original languageEnglish (US)
Title of host publicationMechanics of Solids, Structures, and Fluids
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884607
DOIs
StatePublished - 2020
EventASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online
Duration: Nov 16 2020Nov 19 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume12

Conference

ConferenceASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
CityVirtual, Online
Period11/16/2011/19/20

Keywords

  • LSTM
  • Machine learning
  • Nonlinear behavior modeling
  • Structural brace response behavior

ASJC Scopus subject areas

  • Mechanical Engineering

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