基于Cadna/A与现场测试数据的城市轨道交通环境噪声混合预测方法OA

[Objective]This study aims to address difficulties in determining source strength,complex propagation paths,and insufficient accuracy in urban rail transit noise prediction.An efficient and accurate hybrid prediction method was proposed.[Methods]Field noise monitoring was conducted on the elevated section of Beijing Subway Line 13.Based on Cadna/A software,standard formulas,numerical simulations,and experimental data were integrated,and background noise,wheel-rail noise,and bridge structure-borne noise sources were validated and optimized using multidimensional measured data from multiple measurement points.[Results]The predicted results agreed well with the measured values,with overall errors controlled within 3 dB(A)and errors in front of sensitive buildings within 2 dB(A).Background noise and secondary noise from bridge structures significantly affected prediction accuracy and must be fully considered in the model.[Conclusion]The proposed method overcomes the limitations of traditional prediction approaches,combining accuracy with efficiency.It can be applied to noise impact assessment for sensitive buildings and to the analysis of the noise reduction effectiveness of noise barriers,demonstrating strong engineering applicability.