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An artificial neural network approach to modelling absorbent asphalts acoustic properties

机译:一种模拟吸收剂沥青声学特性的人工神经网络方法

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Sound-absorbing asphalts are particularly useful for reducing noise emissions from vehicular traffic. This solution is perfectly suited for urban areas, in fact the use of sound-absorbing asphalt represents a noise control measure with a negligible environmental impact. In the present work, the results of an experimental investigation on sound-absorbing asphalts were reported. First, the characteristics of the sound-absorbing asphalts used were experimentally found. Then, the measurements of the sound absorption coefficient of the asphalt specimens were investigated. In the final part, numerical simulation model with artificial neural networks of the acoustic coefficient were compared with the data obtained from the measurements. The neural network model showed good Pearson correlation coefficient values (0.894) which can be used with good accuracy to predict the sound absorption coefficient.
机译:吸音沥青特别适用于减少车​​辆流量的噪声排放。该解决方案非常适合城市地区,实际上使用吸音沥青表示噪声控制措施,环境影响可忽略不计。在本作工作中,报道了对吸音沥青的实验研究结果。首先,通过实验发现所使用的吸音沥青的特征。然后,研究了沥青标本的吸声系数的测量。在最终部分中,与从测量获得的数据进行比较了具有声系数的人工神经网络的数值模拟模型。神经网络模型显示出良好的Pearson相关系数值(0.894),其可以以良好的精度使用以预测吸声系数。

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