Identification of Key Factors Influencing Sound Insulation Performance of High-Speed Train Composite Floor Based on Machine Learning

Abstract

The body of a high-speed train is a composite structure composed of different materials and structures. This makes the design of a noise-reduction scheme for a car body very complex. Therefore, it is important to clarify the key factors influencing sound insulation in the composite structure of a car body. This study uses machine learning to evaluate the key factors influencing the sound insulation performance of the composite floor of a high-speed train. First, a comprehensive feature database is constructed using sound insulation test results from a large number of samples obtained from laboratory acoustic measurements. Subsequently, a machine learning model for predicting the sound insulation of a composite floor is developed based on the random forest method. The model is used to analyze the sound insulation contributions of different materials and structures to the composite floor. Finally, the key factors influencing the sound insulation performance of composite floors are identified. The results indicate that, when all material characteristics are considered, the sound insulation and surface density of the aluminum profiles and the sound insulation of the interior panels are the three most important factors affecting the sound insulation of the composite floor. Their contributions are 8.5%, 7.3%, and 6.9%, respectively. If only the influence of the core material is considered, the sound insulation contribution of layer 1 exceeds 15% in most frequency bands, particularly at 250 and 500 Hz. The damping slurry contributed to 20% of the total sound insulation above 1000 Hz. The results of this study can provide a reference for the acoustic design of composite structures.

Keywords

• high-speed train
• composite structure
• sound insulation
• key factor identification
• machine learning
• contribution analysis