The perception of structure-borne noise is particularly salient when train passes through the tunnel under the buildings, which has a negative impact on human health. In the process of constructing buildings along metro lines, it is crucial to estimate indoor structure-borne noise levels in order to enhance design and prevent any negative impact on human comfort. This study conducted measurements of structure-borne noise, reverberation time, and train-induced vibrations in Guangzhou, China to investigate the generation, propagation, and dissipation mechanisms of structure-borne noise. An approach based on Short-Time Fourier Transform and Schroeder integral was proposed for obtaining frequency-dependent reverberation time. Additionally, a deep learning-based approach incorporating indoor vibrations, frequency-dependent reverberation time, and room parameters as inputs was proposed based on Genetic Algorithm-Artificial Neural Network. The estimated structure-borne noise levels demonstrated good agreement with measured values, indicating the feasibility of the approach. The finding of this research facilitates a clear comprehension of the generation, distribution, and dissipation mechanisms of indoor structure-borne noise for engineers while also enabling convenient acquisition of indoor structure-borne noise. The estimated noise levels can be effectively utilized during building design processes along metro lines to mitigate adverse impacts on human comfort.
Keywords: Acoustics-vibration coupling; Measurement; Noise estimation; Structure-borne noise; Vibration and noise correlation.
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