Inverse design of layered periodic wave barriers based on deep learning. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Chen-Xu; Yu, Gui-Lan
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 11(2022)
• Page Start: 2255
• Page End: 2268
• Publication Date: 2022-11
• Subjects: Layered periodic structure -- wave barrier -- inverse design -- deep learning -- frequency attenuation domain -- vibration isolation; Materials -- Periodicals; Engineering design -- Periodicals; 620.11
• DOI: 10.1177/14644207211016886
• Languages: English
• ISSNs: 1464-4207

Abstract

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.