Prediction of nonlinear structural response under wind loads using deep learning techniques
Prediction of nonlinear structural response under wind loads using deep learning techniques
复制标题
DOI：
10.1016/j.asoc.2022.109424
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
S. P. Hareendran;A. Alipour
中科院分区：
文献类型：
--
作者：
S. P. Hareendran;A. Alipour
风对建筑物的作用本质上是连续的，从几分钟到几个小时不等。这种持续时间长、强度大的风会将结构推入后弹性结构范围，导致结构的非线性行为。因此，与基于性能的地震工程不同，结构模拟需要作用几秒钟的地震荷载，而基于性能的风工程(PBWE)的模拟需要作用更长时间的风荷载模型。然而，高层建筑的三维非线性模型将包含数以千计的连接和结构构件，这使得它成为一个复杂的有限元模型。在长达数小时的荷载作用下，这种模型的动态时程分析可能会很繁琐，而且往往无法达到收敛。使用数据驱动技术，利用有限的数值和现场数据来获得长期荷载下的准确结构响应，是一种令人兴奋的选择。深度学习技术在结构健康监测和地震工程研究中得到了广泛的应用。然而，这种数据驱动技术的实施非常有限，在高层建筑风动力相关问题上具有探索的潜力。本文旨在利用深度学习模型来预测高层建筑在持续风荷载作用下的非线性结构反应。使用长短期记忆(LSTM)架构来评估数据驱动方法取代计算密集型三维有限元分析的效率。该架构将在一座150米高的建筑上进行测试，以预测在长时间风荷载下的响应。根据爱荷华州立大学风模拟和测试实验室(WIST)进行的实验研究，将通过预测比例气动弹性模型的加速度响应历史来进一步评估该体系结构的健壮性。
The wind actions on buildings are continuous in nature and could range from a few minutes to more than hours. Such long duration high intensity winds can push the structure to enter post elastic structural range and cause nonlinear behavior. Therefore, unlike performance-based earthquake engineering, where the structural simulations require seismic loads acting for a few seconds, the simulations for performance-based wind engineering (PBWE) requires wind load models acting for much longer durations. However, the nonlinear 3D model of a tall building will contain thousands of connections and structural members making it a complex finite element model. Dynamic time history analysis of such a model under loads lasting for hours can be tedious and often fail to achieve convergence. Using data-driven techniques that utilizes limited numerical and field data to obtain accurate structural responses under long duration loads is an exciting alternative. Deep learning techniques have been extensively used in the studies for structural health monitoring and earthquake engineering. However, the implementation of such data-driven techniques is very limited and has the potential for exploration in problems related to wind dynamics on tall buildings. This paper aims to predict the nonlinear structural response of tall buildings under sustained durations of wind loads using deep learning models. A Long Short-term Memory (LSTM) architecture is used to assess the efficiency of data-driven methods to replace computationally intensive 3D finite element analyses. The architecture will be tested on a 150 m tall building for response predictions under long duration wind loads. The robustness of the architecture will be further evaluated with predicting the acceleration response history of a scaled aeroelastic model based on experimental studies conducted at the Wind Simulation and Testing Laboratory (WiST) at Iowa State University.