doi:10.16186/j.cnki.1673-9787.2020060076

Received:2020/06/28

Revised:2020/10/24

Published:2022/05/15

Research on asphalt mixture grading detection based on BP neural network

GONG Xing , YING Hong , JIANG Peng

School of Architecture and Transportation Engineering，Guilin University of Electronic Technology， Guilin 541004，Guangxi，China

Abstract: To accurately and easily detect the design gradation of asphalt mixture , the electronic sieving method was improved based on image processing, and the planar shapes of the aggregate particles was divided into three categories to obtain statistics of the planar gradation of asphalt mixture. The BP neural network was used to detect the design gradation of asphalt mixture,the planar gradation of coarse aggregates with different particle sizes was used as the input layer,and the design gradation was used as the output layer,200 of normalized planar gradations were trained and tested for neural networks. The results showed that the improved electronic sieving method had higher accuracy than the direct equivalent diameter method and the equivalent ellipse short axis method. The average accuracy of each particle size after detecting the design gradation using BP neural network were:88.1%(4.75 mm),91.2%(9.5 mm),93.8%(13.2 mm),95.1%(16 mm),100%(19 mm),respectively, and the correlation between planar gradation and design gradation was good. The proposed method could provide a new idea for the detection of asphalt mixture gradation.

Key words:asphalt mixture;image processing;electronic sieving;BP neural network;gradation detecting

基于BP神经网络的沥青混合料级配检测研究_宫兴.pdf