This study presents an approach based on deep learning to design layered periodic wave barriers with consideration of typical range of soil parameters. Three cases are considered where P wave and S wave exist separately or simultaneously. The deep learning model is composed of an autoencoder with a pretrained decoder which has three branches to output frequency attenuation domains for three different cases. A periodic activation function is used to improve the design accuracy, and condition variables are applied in the code layer of the autoencoder to meet the requirements of practical multi working conditions. Forty thousand sets of data are generated to train, validate, and test the model, and the designed results are highly consistent with the targets. The presented approach has great generality, feasibility, rapidity, and accuracy on designing layered periodic wave barriers which exhibit good performance in wave suppression in targeted frequency range.

这项研究提出了一种基于深度学习的方法，该方法设计了考虑到典型土壤参数范围的分层周期波屏障。考虑三种情况，P波和S波分别存在或同时存在。深度学习模型由具有预训练解码器的自动编码器组成，该解码器具有三个分支，可以针对三种不同情况输出频率衰减域。定期激活功能用于提高设计精度，并且条件变量被应用在自动编码器的代码层中，以满足实际的多种工作条件的要求。生成了4万套数据来训练，验证和测试模型，并且设计结果与目标高度一致。所提出的方法具有很大的通用性，可行性，快速性，