Predicting Nonlinear Seismic Response of Structural Braces Using Machine Learning

TL;DR

This paper demonstrates that deep learning, especially LSTM networks, can effectively model the complex nonlinear seismic behavior of structural braces, offering a promising approach for engineering applications.

Contribution

It introduces the use of LSTM neural networks for modeling nonlinear seismic responses of structural braces, validating their effectiveness with experimental data.

Findings

LSTM outperforms other ML algorithms in capturing nonlinear behavior

Proper data preparation is crucial for model accuracy

Hyperparameter tuning significantly affects model performance

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.