Decision tree sound insulation prediction model for side wall structure of high-speed train
In order to achieve rapid and accurate sound insulation prediction for the composite structure of high-speed train side walls,we have built a sound insulation sample database based on a substantial amount of pre-existing measured data.Furthermore,a sound insulation prediction model using a decision tree algorithm has been proposed.The model establishes the mapping relationship between the sound insulation of the structure and the material parameters.Firstly,the source of the original measured samples is introduced,and the materials are analyzed,organized,and classified.Next,the main factors affecting the sound insulation performance of the sidewall composite structure are analyzed,and significant features are selected.Finally,the model is trained and validated using the decision tree algorithm in machine learning,and a comparison is made with the traditional finite element-statistical energy analysis(FE-SEA)prediction method.The results show that compared to the traditional FE-SEA model,this decision tree learning model significantly improves the accuracy and efficiency of predicting the sound insulation characteristics of high-speed train sidewall composite structures.With the inclusion of more samples in the future,the model can be further improved and perfected,making it highly practical and promising for engineering applications.
High-speed trainSide wall structureSound insulation predictionDecision tree
NETL
NSTL
万方数据
