This paper presents a new approach for nonlinear multi-component seismic response prediction of structures using hybrid deep learning techniques. Modern recurrent neural networks map the relationship between acceleration time-series of the base/ground of a building and the superstructure, as a form of nonlinear time-history analysis method. Seismic responses were measured in three components which enables multi-component seismic predictions with adequate deep learning architectures. While long short-term memory (LSTM) neural networks can obtain data from a single component per surrogate model, hybrid convolutional-LSTMs (ConvLSTM) neural networks are utilized for multi-component purposes. A guide for pre-processing data and structuring the architecture of deep neural networks are proposed. Also, two filtering methods are compared, Fast Fourier Transform (FFT) Butterworth filter and discrete wavelet transform (DWT) decomposition. Decimation is implemented to reduce the features to useful values, as a dimension reduction approach. With enhancements to the architecture of the network, training time can be reduced significantly, and accuracy could be further improved. A challenging case study is addressed that covers an industrial level practical building. Results show that proposed hybrid models can even predict the capacity curves of a structure indirectly, providing new prospects for engineers to evaluate the seismic performance of a building.

本文提出了一种使用混合深度学习技术进行结构非线性多分量地震反应预测的新方法。现代的递归神经网络以非线性时间历史分析方法的形式，绘制了建筑物基础/地面的加速时间序列与上层建筑之间的关系。在三个分量中测量了地震响应，这使得能够使用适当的深度学习架构来进行多分量地震预测。虽然长短期记忆（LSTM）神经网络可以从每个代理模型的单个组件获取数据，但混合卷积LSTM（ConvLSTM）神经网络却被用于多组件目的。提出了预处理数据和构建深度神经网络架构的指南。此外，还比较了两种过滤方法，快速傅里叶变换（FFT）巴特沃思滤波器和离散小波变换（DWT）分解。作为降维方法，执行抽取以将要素缩小为有用的值。通过增强网络的体系结构，可以显着减少培训时间，并可以进一步提高准确性。解决了一个具有挑战性的案例研究，该案例涵盖了工业级的实用建筑。结果表明，提出的混合模型甚至可以间接预测结构的承载力曲线，为工程师评估建筑物的抗震性能提供了新的前景。通过增强网络的体系结构，可以显着减少培训时间，并可以进一步提高准确性。解决了一个具有挑战性的案例研究，该案例涵盖了工业级的实用建筑。结果表明，提出的混合模型甚至可以间接预测结构的承载力曲线，为工程师评估建筑物的抗震性能提供了新的前景。通过增强网络的体系结构，可以显着减少培训时间，并可以进一步提高准确性。解决了一个具有挑战性的案例研究，该案例涵盖了工业级的实用建筑。结果表明，提出的混合模型甚至可以间接预测结构的承载力曲线，为工程师评估建筑物的抗震性能提供了新的前景。