Determination Model of SBS Content in Modified Asphalt Based on DNN

被引:0
|
作者
Wang Z. [1 ,2 ]
Li J. [1 ]
机构
[1] Highway School, Chang'an University, Xi'an
[2] Guangdong Hualu Transportate Technology Co., Ltd., Guangzhou
来源
Jianzhu Cailiao Xuebao/Journal of Building Materials | 2021年 / 24卷 / 03期
关键词
Accuracy; Depth neural network; Determination model; Fourier transform infrared spectroscopy; Modified asphalt; Road engineering;
D O I
10.3969/j.issn.1007-9629.2021.03.025
中图分类号
学科分类号
摘要
In order to determinate the content of styrene butadiene styrene block copolymer(SBS) in SBS modified asphalt accurately, the Fourier transform infrared spectroscopy(FTIR) spectra of modified asphalts containing different SBS contents were collected by using FTIR instrument, and the determination model for SBS content in modified asphalt was established based on deep neural network(DNN). The influences of different factors on the accuracy of the determination model were studied, and the accuracy, susceptibility and applicability of the model were evaluated. The results show that mean square error of the SBS content determination model is reduced by 70% by dimension reduction and noise reduction. Determination accuracy for SBS content in modified asphalt using DNN method compares favourably with that using standard curve method and random forest method. It also has good sensitivity and applicability to determination of SBS content in modified asphalt by the DNN determination model. © 2021, Editorial Department of Journal of Building Materials. All right reserved.
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页码:630 / 636
页数:6
相关论文
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