In previous in-situ measurements of metro trains it has been found that the velocity level on the track or tunnel wall may vary significantly between different train passages, even though the measuring section, the type of trains and the track and tunnel conditions are identical. An investigation is carried out into the sources of this variability, using a 3D train-track numerical model. This is built using the software SIMPACK and ABAQUS, and is connected through one-way coupling to a finite element model of the tunnel and soil. These models are used to study the influence of various train parameters, including the wheel and rail unevenness, train speed and degree of train loading. For comparison, in-situ measurements were made of the dynamic response of the rail and tunnel wall. The rail roughness at the site as well as the wheel unevenness of all 48 wheels for one train were measured. The results from the model indicate that the wheel unevenness affects the rail velocity level in the frequency region between 25 and 250 Hz and tunnel wall vibration above 5 Hz. The rail velocity level can vary by up to 20 dB due to wheel unevenness, with the largest variations occurring in the frequency bands 50–63 Hz. Variations in passenger loading affect the train-induced vibration by up to 4.5 dB, mainly in the low frequency region. When the train speed varies within a range of ±20% relative to the nominal speed 60 km/h, the frequencies of the peaks are shifted and the level in some frequency bands can change by as much as 10 dB. However, the largest influence is that of the wheel unevenness. It is concluded that the variation in these parameters, especially the wheel and rail unevenness, should be considered to achieve reliable predictions of train-induced vibration.
The operation of a large-scale metro system creates problematic interior noise; the impact of this noise on passengers and drivers is a subject of increasing concern. To investigate the quantitative relationship between metro interior noise and passengers’ annoyance, this study analyzed questionnaires on passenger annoyance completed by 118 volunteers. The feedback from the questionnaire concerned eleven metro lines in Beijing. To test the interior noise levels, the volunteers were divided into two groups: A and B. The volunteers in group A took the same metro train as the testers, whereas those in group B took different trains. A total of 2080 noise annoyance samples from metro tunnel sections were collected and analyzed. Finally, the exposure-response relationship between interior noise and passenger annoyance was obtained by fitting these data with a logistic function. The results indicated that there was a significant positive correlation between the average subjective annoyance and the averaged equivalent sound pressure level. The fitting result was better for group A than for group B. For the mixed samples of two groups, the fitting result was greatly affected by the contribution of group A. To provide an acoustically comfortable environment, metro interior noise should not exceed 84–85 dB(A).
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