Monitoring and predicting seismic responses of a civil structure can be used to assess its behavior under dynamic loading and to determine its structural health condition. In practice, the employed number of sensors is generally limited by the cost and functionality issues. This paper develops a practical solution of four-stage procedures, focusing on prediction of seismic displacement responses at all building floors using acceleration measurements at the optimized sensor locations. In this paper, a novel multi-scale attention-based recurrent neural network is proposed. In particular, the attention mechanisms in the network effectively focuses on more relevant input data among bidirectional ground accelerations and multivariate acceleration responses. The seismic response data for training the developed neural network is generated by performing nonlinear time historical analysis of a three-dimensional finite element model. A floor displacement warping loss is designed to numerically measure the discrepancy between the prediction and the ground truth. A case study is performed using the numerical and real-world data of a high-rise building to systematically evaluate the prediction performance of the proposed methodology. Results demonstrate that the proposed method outperforms the compared state-of-the-art methods in terms of prediction accuracy and reliability.

监视和预测土木结构的地震响应可用于评估其在动态载荷下的行为并确定其结构健康状况。实际上，所采用的传感器数量通常受成本和功能问题的限制。本文提出了一个四阶段程序的实用解决方案，重点是使用优化传感器位置的加速度测量来预测所有建筑物楼层的地震位移响应。本文提出了一种新型的多尺度基于注意力的递归神经网络。特别是，网络中的注意力机制有效地集中于双向地面加速度和多元加速度响应之间的更多相关输入数据。通过对三维有限元模型进行非线性时间历史分析，生成了用于训练发达神经网络的地震响应数据。地板位移翘曲损失旨在对预测值与地面真实情况之间的差异进行数值测量。使用高层建筑的数字和实际数据进行案例研究，以系统地评估所提出方法的预测性能。结果表明，在预测准确性和可靠性方面，该方法优于最先进的方法。使用高层建筑的数字和实际数据进行案例研究，以系统地评估所提出方法的预测性能。结果表明，在预测准确性和可靠性方面，该方法优于最先进的方法。使用高层建筑的数字和实际数据进行案例研究，以系统地评估所提出方法的预测性能。结果表明，在预测准确性和可靠性方面，该方法优于最先进的方法。