基于POS-LSSVM的沥青路面性能评价方法

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.

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

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 results show that the method is feasible and effective for evaluation of asphalt pavement performance.