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

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.