Long short-term memory for nonlinear static analysis of functionally graded plates

  • Dieu T. T Do Duy Tan Research Institute for Computational Engineering, Duy Tan University, 254 Nguyen Van Linh street, Da Nang, Vietnam
  • Son Thai Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam c
  • Tinh Quoc Bui Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan
Keywords: long short-term memory, time series forecasting, nonlinear analysis, functionally graded plate


This study presents an effective method based on long short-term memory to reduce the computational cost in nonlinear static analysis of functionally graded plates. Data points representing a load-deflection curve in a dataset are generated through isogeometric analysis (IGA). The order of these data points is always maintained as a sequential series of observations; therefore, it is referred to as a time series. Dataset is divided into three sets including training, testing, and prediction sets. Both training and testing sets are used for the training process by the long short-term memory to gain optimum weights. Based on these obtained weights, data points in the prediction set are directly predicted without using any analysis tools. The effectiveness and accuracy of the proposed method are demonstrated by comparing the obtained results to those of isogeometric analysis.


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How to Cite
Do, D., Thai, S., & Bui, T. Q. (2022). Long short-term memory for nonlinear static analysis of functionally graded plates. Journal of Science and Technology in Civil Engineering (STCE) - HUCE, 16(3), 1-17. https://doi.org/10.31814/stce.huce(nuce)2022-16(3)-01
Research Papers