...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The international journal of pavement engineering >Evaluation of cracking in asphalt pavement with stabilized base course based on statistical pattern recognition
【24h】

Evaluation of cracking in asphalt pavement with stabilized base course based on statistical pattern recognition

机译：基于统计模式识别的稳定基层沥青路面开裂评价

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Selection of optimal treatment strategy for a cracked asphalt pavement requires efficient evaluation of cracking. Pavement cracking severity was usually evaluated just by crack width in the past. In this paper, a novel method for extracting both crack type and crack width information in asphalt pavement with chemically stabilised base course from falling weight deflectometer (FWD) test data based on k-nearest neighbour (k-NN) pattern recognition was put forward. FWD field tests and core drilling tests were conducted at cracks on four typical expressways and a database with 215 deflection basins was established. Three feature sets were selected or extracted from each deflection basin as inputs. And, cracks were categorised into six classes based on crack type and crack width as target outputs. Results indicated that most cracks with high width level were thermal cracks and it was of great importance to consider both crack type and width in cracking evaluation in asphalt pavement with chemically stabilised base. Considering the classification accuracy and the convenience of test, crack was advised to be located between sensor S-300 and sensor S-600 in FWD field test. Deflection ratio feature set was recommended as inputs of the k-NN classifier.
机译：选择开裂沥青路面的最佳处理策略需要对开裂进行有效评估。过去通常仅通过裂缝宽度来评估路面开裂的严重程度。提出了一种基于k近邻（k-NN）模式识别的落差偏折仪（FWD）测试数据提取化学稳定基层沥青路面裂缝类型和裂缝宽度信息的新方法。在四个典型的高速公路上的裂缝处进行了FWD现场测试和岩心钻探测试，并建立了具有215个偏转盆地的数据库。从每个偏转盆地中选择或提取了三个特征集作为输入。并且，基于裂纹类型和裂纹宽度作为目标输出，将裂纹分为六类。结果表明，大多数宽幅裂缝为热裂缝，在化学稳定基础的沥青路面裂缝评估中，同时考虑裂缝类型和宽度非常重要。考虑到分类的准确性和测试的便利性，建议在FWD现场测试中在传感器S-300和传感器S-600之间放置裂纹。建议将变形比功能集作为k-NN分类器的输入。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号