Engineering and TechnologyNeural Networks
Response Prediction of Nonlinear Hysteretic Systems by Deep Neural Networks
T. Kim, O. Kwon, et al.
Taeyong Kim, Oh-Sung Kwon, and Junho Song present a deep neural network approach that learns from nonlinear time history analyses to predict the response of nonlinear hysteretic systems under stochastic excitations. Applied to earthquake engineering, the method outperforms simplified regression equations and offers a faster alternative for estimating structural responses without costly simulations.
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