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

Dang Viet Hung, N. Thang
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引用次数: 2

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.
用时间替代模型预测随机风荷载作用下框架结构的动力响应
确定结构的动力响应是一个具有挑战性和耗时的问题,因为它需要以非常小的时间步长迭代求解运动控制方程以确保结果收敛。本研究提出了一种基于深度学习范式的替代方法,该方法与传统方法(如有限元方法)互补,可以以合理的精度快速预测随机风荷载下结构的响应。该方法以序列到序列的方式工作,在预测性能和所需的计算资源之间提供了良好的权衡。已知的风荷载序列加上结构的时程响应,在经过深度学习模型之前被聚合成一个三维张量输入,该模型包括长短期记忆层和时间分布层。模型的输出是结构未来响应的序列,它随后将被用作计算结构下一个响应的输入。通过一个二维三湾九层钢筋混凝土框架结构的实例证明了所提出方法的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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