Analysis of Reduction Effectiveness and Design of Locally Resonant Metamaterial Barriers for Train Vibration

Train vibrations are the primary concern in environmental engineering and civil engineering. It is significantly imperative to find new methods for reducing and isolating vibrations. The locally resonant metamaterials (LRMs) propose a novel method and concept for reducing train vibration. However, the accurate and quick design structures of LRMs based on vibration characteristics are still an issue. Thus, this study presents a novel inverse design model of three-component locally resonant metamaterial barriers (LRMBs) for vibration reduction based on deep learning. The bandgap characteristics and vibration modes of the LRMB are investigated by using the improved plane wave expansion (IPWE) and finite element method (FEM). Besides, the gradient-combined LRMBs are proposed based on time–frequency features of measured vibration caused by trains and the novel inverse design model, and a two-dimensional finite element model coupling with infinite element boundaries is established to study the reduction efficiency of the gradient-combined LRMBs. And the performances of different LRMBs are fully analyzed in time and frequency domains. The results show that the novel inverse design model can be successfully used to design the LRMB based on vibration features. Moreover, the gradient-combined LRMBs have better isolation performance.