基于POS-LSSVM的沥青路面性能评价方法  

A Method of Asphalt Pavement Performance Evaluation Based on POS-LSSVM

作　　者：颜可珍[1,2] 吴建良[1] 

机构地区：[1]湖南大学土木工程学院,湖南长沙410082 [2]山区道路建设与维护技术重庆市重点实验室,重庆400074

出　　处：《公路交通科技》2009年第12期32-34,100,共4页Journal of Highway and Transportation Research and Development

基　　金：国家自然科学基金资助项目(50808077);中国博士后科学基金资助项目(20070411113);山区道路建设与维护技术重庆市重点实验室开放基金资助项目(200703)

摘　　要：基于支持向量机的结构风险最小化与粒子群算法快速全局优化的特点,用粒子群算法优化最小二乘支持向量机的参数,避免了人为选择参数的盲目性,提高了预测模型的训练速度和预测推广能力。选择PCI、RQI、SSI、BPN,4个指标作为评价指标,建立了基于PSO-LSSVM的沥青路面性能评价模型,将该模型用于路面性能评价,获得了令人满意的评价效果。结果表明,支持向量机法可以可靠有效地评价沥青路面性能。The support vector machine based on statistical learning theory is applied to establish a model for asphalt pavement evaluation. The particle swarm optimization, which can avoid the man-made blindness and enhance the efficiency and capability of forecasting, was used to optimize the parameters of the of least squares support vector machine （LS-SVM） . PCI, RQI, BPN and SSI were selected as evaluation indexes to establish the model for pavement performance evaluation based on PSO-LSSVM. The resuhs show that the method is feasible and effective for evaluation of asphalt pavement performance.

关 键 词：道路工程 路面评价 最小二乘支持向量机 路面 

分 类 号：U416.217[交通运输工程—道路与铁道工程]