Study of the traffic noise source intensity emission model and the frequency characteristics for a wet asphalt road

摘要

The traffic noise source intensity emission model is the basis of traffic noise prediction. In addition, the presence of water on the road is an important factor of road traffic noise. However, to date, none of the traffic noise source intensity emission models account for a situation in which the road surface is wet. Therefore, the main objective of this paper is to build a traffic noise source intensity emission model on a wet asphalt road based on traffic noise measurements, in which speed, sound pressure level and frequency spectrum were recorded as a single vehicle passed by. By using statistical analysis on the measurement data, a traffic noise source intensity emission model on a wet asphalt road was acquired, which conforms to correlation test, F-test and f-test. The result shows that the sound pressure level increases considerably with the presence of water on asphalt road for all types of vehicles; the mean difference in the sound pressure level between the wet and dry asphalt roads for light, middle-size, and heavy vehicles are 10.09 dB(A), 5.56 dB(A), and 4.26 dB(A), respectively. In addition, the noise difference between a wet and dry asphalt road decreases as the speed and vehicle size grow. Furthermore, the frequency characteristics of the road traffic noise were analysed through the sound pressure level and noise energy percentage in the 1/3 octave band spectrum. The result shows that for the wet asphalt road, the sound pressure level is high at high frequency and low at low frequency, which is completely different from the response on a dry asphalt road. In addition, for a dry asphalt road, the vehicle's noise energy percentage at low frequency decreases as the speed increases, whereas at high frequency, the percentage increases as the speed grow. However, for a wet asphalt road, the noise energy percentage changes little with speed. The findings can be applied to the accurate traffic noise prediction and noise control, especially in the rainy regions. (C) 2017 Elsevier Ltd. All rights reserved.