基于神经网络的土密实度检测  

Detection of doil compactness based on neural networks

作　　者：李细荣[1] 

机构地区：[1]闽南师范大学物理与信息工程学院,福建漳州363000

出　　处：《甘肃农业大学学报》2015年第4期175-180,共6页Journal of Gansu Agricultural University

基　　金：江西省教育厅科技青年基金项目"基于激光图像技术的路基压实度的无损检测研究"(GJJ13695)

摘　　要：为了通过神经网络预测土密实度,搭建了土密实度检测装置,采集了土的激光图像,并提取了土的吸收系数、散射系数、激光图像的纹理特征参量和图像的灰度变化率共13个特征参数,通过SPSS降维处理,提取了5个主成分因子作为输入特征;并利用BP神经网络预测密实度.结果表明:经过31次测试后达到了误差要求,预测值与环刀法结果比较的平均绝对误差为7.14%和平均相对误差为7.71%,所建立的预测模型可行;最后通过试验对预测模型进行验证,其预测值与环刀法结果比较的平均绝对误差为8.62%和平均相对误差为8.76%,说明用神经网络预测土密实度是可行的.In order to test soil compactness by the neural network, a laser image measurement system of soil compactness was established. The thirteen parameters were collected from each image, such as the ab- sorption coefficient, scattering coefficient,texture features and image gray change rate. The SPSS was per- formed to select five characteristic parameters,and soil compactness was predicted by BP neural networks. The results showed that BP neural networks reached the required error after 31 loops. Compared with the measurement values by using round knife method, the average absolute error was 7. 14%, and average rela- tive error was 7.71%, the BP neural network was feasible. The predictive model was verified, the predictive value with cutting ring results in an average absolute error was 8. 62%, and average relative error was 8.76%. It is suggested that soil compactness by the neural network is feasible.

关 键 词：神经网络 密实度 激光图像 

分 类 号：U416.1[交通运输工程—道路与铁道工程]