基于卷积神经网络的结构地震反应预测

Feiyu Guo, Yinfeng Dong, Hui Tian, Xingyu Zhang, Qingshuang Su
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引用次数: 0

摘要

建筑物的地震反应是结构性能分析的关键。对于具有完整设计数据的结构,可以用有限元分析来预测其地震反应。然而,对于缺乏必要信息的结构,建立有限元模型并预测其地震反应可能具有挑战性。与有限元分析相比,卷积神经网络(cnn)可以在没有设计数据的情况下,建立结构与地震反应之间的神经网络映射关系,从而预测结构的地震反应。本文建立了基于cnn的结构响应预测模型,分析了自振降频对东北地震后结构响应的影响。结构加速度和位移响应的成功预测,为缺乏设计数据的建筑物地震反应预测提供了一种新的分析方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural seismic response prediction based on convolutional neural networks
The seismic response of buildings is crucial for structural performance analysis. For structures with complete design data, the seismic response can be predicted using finite element analysis. However, for structures lacking necessary information, building finite element models and predicting their seismic response can be challenging. Compared to finite element analysis, convolutional neural networks (CNNs) can establish a neural network mapping relationship between the structure and the seismic response to predict the structural response without design data. In this paper, a structural response prediction model based on CNNs is established, aiming to analyze the effect of natural frequency reduction on the structural response after the Tohoku earthquake. The successful prediction of the structural acceleration and displacement response provides a new analytical method for predicting the seismic response of buildings lacking design data.
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