Bio-inspired acoustic metamaterials for traffic noise control: bridging the gap with machine learning
Jing Lu, Siqi Ding, Yi‐Qing Ni, Shu Li
Abstract
Acoustic metamaterials (AMMs) represent a transformative approach to sound manipulation, capable of controlling acoustic waves in ways that are not possible with traditional materials. These materials, often inspired by biological structures, leverage complex geometries and innovative designs to enhance sound absorption and control. This review outlines the fundamentals of bio-inspired AMMs, discusses their design and performance characteristics, and highlights the challenges in translating these innovations into practical applications. We also explore the integration of machine learning (ML) techniques with bio-inspired design to optimize AMM for practical implementation. Finally, we propose future research directions aimed at developing broadband AMMs that effectively address the pressing issue of traffic noise, thereby enhancing the overall efficacy of noise control solutions. Jia-Hao Lu and colleagues explore advancements in bio-inspired acoustic metamaterials for railway noise mitigation. They review design strategies that integrate machine learning techniques to enhance sound absorption and control.