Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network

doi:10.1016/j.conbuildmat.2022.128969

IR > 土木工程学院

	Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network
	Nian, Tengfei1,2 ; Li, Jinggao 1; Li, Ping1 ; Liu, Zongcheng 1; Guo, Rui 3; Ge, Jinguo 1; Wang, Meng 1
	2022-10-10
发表期刊	CONSTRUCTION AND BUILDING MATERIALS
ISSN	0950-0618
卷号	351
摘要	Given the complexity of the factors that affect the interlayer shear strength of asphalt pavement in typical steeply-sloped sections of areas with seasonally frozen soil, to predict and evaluate interlayer shear strength more accurately and rapidly, this study obtains experimental data by designing indoor direct and oblique shear tests, and establishes a three-layer improved back propagation (BP) neural network model to predict the interlayer shear strength of pavement with a structure of 6-20-1. The model uses 6 influencing factors of specimen combination types-tack coat type, a tack coat dosage, shear angle, temperature, and loading rate-as the input layer. The neural network trained, verified, and tested 230 sets of oblique shear test data, and completed the neural network's universality test. The research results show that the predictive value of the shear strength under different viscous layer oil dosages and temperatures is very consistent with the results of universal testing experiments. Different types of specimens' interlayer shear strength increases first and then decrease as the amount of tack coat increases, and compared with base asphalt and emulsified asphalt, SBS-modified asphalt has the best interlayer shear resistance when used as the tack coat, and the optimal dosage is 1.2 kg/m2. Temperature is under a significant negative correlation with the specimen's interlayer shear strength. As the temperature rises from 20 to 60 degrees C, the interlayer shear strength of different specimen types decreased at a faster rate, and the shear strength at 58 degrees C was only 15 %-30 % of that at 20 degrees C. The constructed BP neural network prediction model has good convergence and superior performance. The model prediction error does not exceed +/- 0.5, and the prediction accuracy is high (R2 = 0.99). At the same time, the shear specimen's shape does not affect the use of the interlayer shear strength prediction model, which can be utilized to predict the interlayer shear strength of the asphalt mixture specimens.
关键词	Road engineering Asphalt mixture Interlayer shear strength BP neural network Prediction method
DOI	10.1016/j.conbuildmat.2022.128969
收录类别	SCIE ; EI
语种	英语
WOS研究方向	Construction & Building Technology ; Engineering ; Materials Science
WOS类目	Construction & Building Technology ; Engineering, Civil ; Materials Science, Multidisciplinary
WOS记录号	WOS:000852332900003
出版者	ELSEVIER SCI LTD
EI入藏号	20223512666971
EI主题词	Asphalt mixtures
EI分类号	406.2 Roads and Streets ; 411.1 Asphalt ; 483.1 Soils and Soil Mechanics ; 723.4 Artificial Intelligence ; 802.3 Chemical Operations
来源库	WOS
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/159835
专题	土木工程学院
通讯作者	Nian, Tengfei; Li, Ping
作者单位	1.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou, Gansu, Peoples R China; 2.Gansu Rd & Bridge Construction Grp Maintenance Tec, Lanzhou, Gansu, Peoples R China; 3.Shaanxi Univ Technol, Sch Civil Engn & Architecture, Hanzhong, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Nian, Tengfei,Li, Jinggao,Li, Ping,et al. Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network[J]. CONSTRUCTION AND BUILDING MATERIALS,2022,351.
APA	Nian, Tengfei.,Li, Jinggao.,Li, Ping.,Liu, Zongcheng.,Guo, Rui.,...&Wang, Meng.(2022).Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network.CONSTRUCTION AND BUILDING MATERIALS,351.
MLA	Nian, Tengfei,et al."Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network".CONSTRUCTION AND BUILDING MATERIALS 351(2022).