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

doi:10.1016/j.conbuildmat.2022.128969

CORC > 兰州理工大学 > 兰州理工大学 > 土木工程学院

	Method to predict the interlayer shear strength of asphalt pavement based on improved back propagation neural network
	Nian, Tengfei 1,3; Li, Jinggao 1; Li, Ping 1; Liu, Zongcheng 1; Guo, Rui 2; Ge, Jinguo 1; Wang, Meng 1
刊名	CONSTRUCTION AND BUILDING MATERIALS
	2022-10-10
卷号	351
关键词	Road engineering Asphalt mixture Interlayer shear strength BP neural network Prediction method
ISSN号	0950-0618
DOI	10.1016/j.conbuildmat.2022.128969
英文摘要	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.
WOS研究方向	Construction & Building Technology ; Engineering ; Materials Science
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000852332900003
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159835]
专题	土木工程学院
作者单位	1.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou, Gansu, Peoples R China; 2.Shaanxi Univ Technol, Sch Civil Engn & Architecture, Hanzhong, Peoples R China 3.Gansu Rd & Bridge Construction Grp Maintenance Tec, Lanzhou, Gansu, Peoples R China;
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).