基于PCA-PSO-SVM的沥青路面使用性能评价

Evaluation of asphalt pavement performance based on PCA⁃PSO⁃SVM

为合理地对沥青路面使用性能进行综合评价,针对传统路面使用性能评价方法主观性强以及已有模型存在缺陷等问题,提出了基于主成分分析-粒子群优化-支持向量机(PCAPSO-SVM)的评价模型。通过主成分分析(PCA)对评价指标进行降维处理,形成彼此相互独立的主成分,利用粒子群算法(PSO)全局搜索优势对支持向量机(SVM)的关键参数——惩罚系数C和核函数参数g进行优化,以提高模型精度。最后,以西南地区某高速公路170个养护路段为例,分别使用PCA-PSO-SVM模型和《公路技术状况评定标准》对路面性能进行综合评价。结果表明,PCA-PSO-SVM模型克服了依靠经验确定参数的缺点,识别精度提高,评价结果更符合实际路况。

In order to reasonably conduct a comprehensive evaluation of asphalt pavement performance,a PCA-PSO-SVM evaluation model based on principal component analysis and particle swarm optimization support vector machine is proposed to address the problems of the strong subjectivity of traditional pavement performance evaluation methods and the defects of existing models.The evaluation indicators are dimensionally reduced by Principal Component Analysis(PCA)to form mutually independent principal components.The key parameters of the Support Vector Machine(SVM)—the penalty coefficient C and the kernel function parameter g are optimized using the global search advantage of the Particle Swarm Algorithm(PSO)to improve model accuracy.Finally,170 maintenance sections of a highway in the southwest region were used as an example to evaluate the pavement performance comprehensively using the PCA-PSO-SVM model and“Highway performance assessment standards”respectively.The results show that the PCA-PSO-SVM model overcomes the drawbacks of relying on empirical methods of determining parameters,improves the identification accuracy and makes the evaluation results more in line with the actual road conditions.