摘要
为提高沥青路面的检测效率,以某沥青路面某桩号断面的路面沉陷数据为研究对象,基于BP神经网络,对高速公路沥青路面沉陷发展进行了拟合及预测。试验结果表明,BP神经网络模型能够有效预测路面沉陷,随着训练组数据的增加,神经网络模型的预测精度不断提高;基于工程效率和预测精度方面的考虑,建议选用32组数据作为最佳样本数;BP神经网络模型的预测精度显著高于二次曲线法的,相对误差降低了5%。该研究验证了BP神经网络模型应用于路面沉陷发展预测的可行性和有效性,为探究高速公路沥青路面沉陷发展提供了新方法。
To enhance the efficiency of asphalt pavement inspection,the pavement subsidence data from a specific section and station number of an asphalt road were taken as research targets.A fitting and prediction of the development of asphalt road subsidence on the highway was conducted based on the BP neural network.The results showed that BP neural network model can effectively predict road subsidence.The predictive accuracy of the neural network model was steadily improved with an increase in the training data set.Considering engineering efficiency and predictive accuracy,it was recommended to use 32 sets of data as the optimal sample size.The predictive accuracy of the BP neural network model was significantly higher than that of the quadratic curve method,with a relative error reduction of up to 5%.The study confirmed the feasibility and effectiveness of the BP neural network model in predicting the development of pavement subsidence,providing a new method for investigating the development of asphalt pavement subsidence on highways.
作者
曹阳
杨傲
翟博渊
聂付松
文家刚
CAO Yang;YANG Ao;ZHAI Boyuan;NIE Fusong;WEN Jiagang(Beijing Municipal Road and Bridge Co.,Ltd.,Beijing 100032,China;College of Urban Construction,Wuchang Institute of Technology,Wuhan 430065,China;Beijing Zhongyan Technology Co.,Ltd.,Beijing 100041,China;Central South Exploration and Foundation Engineering Co.,Ltd.,Wuhan 430040,China)
出处
《无损检测》
CAS
2024年第4期48-52,共5页
Nondestructive Testing
基金
湖北省教育厅科学技术研究计划项目厅局级指导项目(B2022353)。
关键词
道路工程
沥青路面沉陷
BP神经网络
预测模型
road engineering
asphalt pavement subsidence
BP neural network
prediction model
