Bio-inspired acoustic metamaterials for traffic noise control: bridging the gap with machine learning.

This project studies how to design, simulate, and improve bio‑inspired acoustic metamaterials that can reduce traffic noise. It uses machine learning to connect the complex shapes of these materials with how well they block sound. Typical noise barriers are heavy, bulky, and only work well over a narrow range of frequencies. By copying natural cellular or hierarchical structures, bio‑inspired metamaterials can be light and block sound more efficiently. The problem is that the many possible shapes make traditional finite‑element analysis (FEA) too slow. To solve this, we propose a machine‑learning‑based optimization framework. First, we run many simulations in COMSOL Multiphysics to generate data on acoustic transmission loss and bandgap behavior

for different geometries. Then we train deep neural networks on that data. The trained networks can quickly predict how a new design will perform, allowing us to work backward from a desired sound‑blocking goal to the best shape parameters. Using these models, we can find the structural settings that give the highest sound transmission loss in the low‑to‑mid frequency range typical of urban traffic noise. The approach offers a systematic way to speed up the creation of next‑generation acoustic barriers, combining bio‑inspired design with artificial intelligence for scalable noise control.

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