Helmholtz resonators (HRs) have the advantage of extending and improving their insulating capacity when used as scatterers in noise barriers made of periodic media, such as sonic crystals (SCs). However, the interaction between multiple Bragg scattering and local resonance phenomena can increase or decrease the insulation of the barrier depending on its design. In the present work, we numerically investigate the factors that determine how such interferences occur and the specific conditions to increase the insertion loss of sonic crystal noise barriers (SCNBs) made of cylindrical scatterers with HRs. Two factors are crucial for the variation of the isolation of the barrier in the Bragg-bandgap (Bragg-BG): the orientation of the resonator mouth with respect to the incident wave, and the resonance frequency of the resonator with respect to the central frequency of the Bragg-BG. Based on this phenomenon, we propose a sonic crystal noise barrier consisting of scatterers with two Helmholtz resonators. The insertion loss of the structure is determined numerically and shows an increase of 20 dB at the BG compared to a conventional barrier with cylindrical scatterers.

The study of technological materials made by meticulously arranging acoustic elements has received a lot of attention over the past three decades with the goal of generating improved acoustic properties, often going beyond the behavior of materials found in nature. These are frequently referred to as acoustic metamaterials, and because of the way wave propagation phenomena is managed, they exhibit unusual properties. Improvements in noise mitigation techniques of acoustic systems based on metamaterials principles have been made effectively and precisely using combined or hybrid numerical methods and improved numerical formulations. These noise mitigation properties should be optimized by modifying topology and inner elements properties, which requires a huge search space. This work focuses on metamaterials called sonic crystals with Helmholtz resonators, and its innovative insulation properties as noise barriers are optimized with the Particle Swarm Optimization (PSO). This evolutionary algorithm provides a mono objective solution for the multi-dimensional search space inspired in animal behavior looking for food and communicating between each other. In order to assess the results obtained by the proposed approach, the presented PSO algorithm is compared with a Genetic Algorithm (GA): the results show that the PSO algorithm provides a better solution that pursues the objective of satisfying the acoustic comfort without exceeding the imposed practical constraints.

Sonic crystal noise barriers (SCNB) have emerged as a promising solution for mitigating traffic noise pollution. These barriers utilize periodic structures to selectively reflect acoustic waves at specific target frequencies, offering the advantage of being permeable to light and wind. However, their installation and maintenance costs have hindered widespread adoption. In contrast, active noise control (ANC) systems leverage speakers and microphones to generate opposing sound waves that cancel out incoming noise, presenting a potentially cost-effective alternative. The efficacy of ANC, however, hinges on the precision of noise prediction models and control algorithms. Reinforcement Learning (RL) technique, an interdisciplinary area of machine learning, has shown promise in enhancing ANC systems by enabling them to adapt to changing noise conditions and achieve superior noise reduction, particularly in enclosed spaces. Despite these advancements, several challenges remain in applying RL to ANC systems for SCNB. This paper explores these challenges and proposes an RL-based solution for autonomous ANC systems within the context of SCNB, utilizing a Finite Difference Time Domain (FDTD) simulation environment to address low-frequency, moving sources, and outdoor propagation noise scenarios.