计算机与数字工程2024，Vol.52Issue(1)：111-115,5.DOI:10.3969/j.issn.1672-9722.2024.01.017
基于卷积长短时记忆网络的国际平整度指标预测
Prediction of International Roughness Index Based on Convolution Long Short Time Memory Network
摘要
Abstract
The rapid development of highway brings the demand for rapid detection and analysis of various pavement indexes.According to the characteristics of international pavement roughness indexes,the combination of convolution neural network and long-term and short-term memory neural network(CNN-LSTM)is proposed to predict the international pavement roughness index-es.Convolution neural network and long-term and short-term memory neural network learn the spatial dimension of lidar range data respectively according to the characteristics of roughness and time dimension,the prediction of flatness index is completed.The ex-perimental results show that,compared with LSTM network,the MAPE value of CNN-LSTM model is only 2.3488,and the accura-cy and recall rate are 90.61%and 87.89%respectively.By comparing the real value with the predicted value,it can be found that CNN-LSTM is more suitable for the prediction of international roughness index.关键词
长短时记忆神经网络/国际平整度预测/卷积神经网络/路面平整度Key words
long short memory neural network/international roughness prediction/convolutional neural network/pavement roughness分类
交通工程引用本文复制引用
黄凯枫,刘庆华..基于卷积长短时记忆网络的国际平整度指标预测[J].计算机与数字工程,2024,52(1):111-115,5.基金项目
国家自然科学基金项目(编号:51008143) （编号:51008143）
江苏省六大高峰人才项目(编号:XYDXX-117)资助. （编号:XYDXX-117）
