| 基于支持向量机的沥青路面使用性能评价 |
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| 引用本文： | 黄冰,颜可珍,张邹.基于支持向量机的沥青路面使用性能评价[J].中南公路工程,2012(2):19-21. |
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| 作者姓名： | 黄冰 颜可珍 张邹 |
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| 作者单位： | [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庆重点实验室,重庆400074 |
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| 基金项目： | 重庆交通大学山区道路建设与维护技术重庆市重点实验室开放基金项目（编号：CQMRCM-07-03） |
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| 摘 要： | 路面使用性能决定了路面维修养护的方法。但沥青路面使用性能4个指标：PCI、SSI、SRI和IRI中路面状况指数检测困难。支持向量机是具有严格统计学习理论的新型学习方法。它对解决小样本非线性等问题具特有的优势。基于支持向量机理论,分析沥青路面使用性能4个指标间的关系,建立了SSI、SRI、IRI实测数据对PCI的预测模型,获得了令人满意的预估效果。结果表明,支持向量机是沥青路面性能评估的简单有效的方法。
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| 关 键 词： | 支持向量机 路面使用性能 路面破损状况 |
Evaluation of Pavement Performance Using Support Vector Machine |
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| HUANG Bing,YAN Kezhen,ZHANG Zou.Evaluation of Pavement Performance Using Support Vector Machine[J].Central South Highway Engineering,2012(2):19-21. |
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| Authors: | HUANG Bing YAN Kezhen ZHANG Zou |
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| Institution: | 1.College of civil engineering,Hunan University,Changsha,Hunan 410082,China;2.Hi-tech Laboratory for Mountain Road Construction and Maintenance,Chongqing Jiaotong University,Chongqing 400074,China) |
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| Abstract: | The method of pavement maintenance is determined by pavement performance.PCI,SSI,SRI and IRI were selected as the asphalt pavement performance evaluation indexes,but it is difficult to get pavement condition index.The support vector machine is a new rigorous statistical learning theory,which is very good at analyzing small samples and non-linear datum.This paper describes the relationships among the four indicators.SSI,SRI and IRI were used for establishing the prediction model to forecast PCI based on SVM.The results show that the method is simple and effective for evaluation of asphalt pavement performance. |
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| Keywords: | Support Vector Machine Pavement Performance PCI |
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