Predicting dynamic responses of frame structures subjected to stochastic wind loads using temporal surrogate model

  • Dang Viet Hung Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam
  • Nguyen Truong Thang Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam
Keywords: dynamics of structures, reinforced concrete, frame, vibration, deep learning, surrogate

Abstract

Determining structures' dynamic response is a challenging and time-consuming problem because it requires iteratively solving the governing equation of motion with a significantly small time step to ensure convergent results. This study proposes an alternative approach based on the deep learning paradigm working in a complementary way with conventional methods such as the finite element method for quickly forecasting the responses of structures under random wind loads with reasonable accuracy. The approach works in a sequence-to-sequence fashion, providing a good trade-off between the prediction performance and required computation resources. Sequences of known wind loads plus time history response of the structure are aggregated into a 3D tensor input before going through a deep learning model, which includes a long short-term memory layer and a time distributed layer. The output of the model is a sequence of structures' future responses, which will subsequently be used as input for computing structure' next response. The credibility of the proposed approach is demonstrated via an example of a two-dimensional three-bay nine-story reinforced concrete frame structure.

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Published
29-04-2022
How to Cite
Hung, D. V., & Thang, N. T. (2022). Predicting dynamic responses of frame structures subjected to stochastic wind loads using temporal surrogate model. Journal of Science and Technology in Civil Engineering (STCE) - HUCE, 16(2), 106-116. https://doi.org/10.31814/stce.huce(nuce)2022-16(2)-09
Section
Research Papers