Deep learning-based topology design of periodic barrier for full-mode waves

A deep learning model is proposed to solve the design problem of periodic wave barrier with consideration of full-mode waves including in-plane mixed mode and out-of-plane shear mode, and the effect of site conditions on design is taken into account. The proposed model is composed of a variational autoencoder (VAE) and an autoencoder (AE) with two pretrained decoders. It can perform designs for different situations and give multiple structures for the same target within a very short time. Large number of targets in the testing set are considered, and the designed results highly meet the expectations. A periodic wave barrier is designed by the approach for a practical example, and the vibrations in main frequency range are attenuated greatly. The deep learning method makes the design of periodic wave barrier smart, efficient, accurate, and universal.

Automated summary

The proposed deep learning model, consisting of VAE and AE, efficiently and accurately solves the design problem of periodic wave barriers, providing satisfactory results and demonstrating strong adaptability.