实验性GPR数据集用于表征多层路面结构 - - 使用混合化方法对层的评估
Grégory Andreoli1, Amine Ihamouten2, David Souriou3
1Gustave Eiffel University, MAST/EMGCU - Champs-sur-Marne, F-13300 Salon-de-Provence, France.
Data in brief
|September 16, 2025
概括
这项研究使用脉冲地面透雷达 (GPR) 来分析路面层和层粘合. 机器学习模型准确地绘制这些特征,改善基础设施监控.
科学领域:
- 土木工程 土木工程是指土木工程.
- 地质物理学 地质物理学
- 材料科学 材料科学 材料科学
背景情况:
- 非破坏性测试 (NDT) 对于路面的验证和监测至关重要.
- 传统的地面透雷达 (GPR) 难以与薄路面层和皮层的特征进行斗争.
- 准确评估层对于确保正确的粘合和路面长寿至关重要.
研究的目的:
- 开发和验证一种方法,以使用脉冲GPR来表征路面的层.
- 创建一个全面的路面结构数据库,有控制的变化用于研究.
- 区分带有和没有毛的区域,并检测微妙的几何变化.
主要方法:
- 在受控路面结构上使用了多个中心频率的脉冲GPR.
- 创建了一个数据库,其中包含了材料,几何形状和皮乳液数量的变化.
- 在数据处理方面探索了一种混合机器学习/全波形逆转 (FWI) 方法.
- 支持矢量机 (SVM) 模型用于数据分类和映射.
主要成果:
- 冲动GPR系统与接地合的带天线在数据采集方面是有效的.
- SVM模型成功地对获得的数据进行了分类,区分了甲的存在.
- 结果被绘制为电磁图像,可视化了路面层特征.
- 该方法对增强路面层特征的增强路面层特征显示出希望.
结论:
- 冲动GPR与机器学习相结合,为详细的路面层分析提供了可行的解决方案.
- 这种方法提高了评估关键薄层 (如衣) 的能力.
- 开发的方法支持改进路面设计,施工质量控制和长期监测.
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