合成GPR数据集用于评估混杂化反向方法的路面层表征-几何和物理参数研究
Grégory Andreoli1, Amine Ihamouten2, Xavier Dérobert3,4
1Gustave Eiffel University, MAST/MIT - Nantes, F-44344 Bouguenais, France.
Data in brief
|July 17, 2025
概括
这项研究使用地面合地面透雷达 (GPR) 和混合机器学习方法来描述路面地下层. 目标是改善检测缺陷,如分层和剥离.
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:
- 路面表面的退化通常源于地下缺陷.
- 路面层之间的粘合质量,特别是磨损和粘合剂流程,对于防止分层和剥离等问题至关重要.
- 非破坏性测试 (NDT) 方法对于评估路面完整性而不会造成损坏至关重要.
研究的目的:
- 开发和验证一种新的混合方法,将机器学习和全波形反转结合起来,用于路面特性.
- 为培训和测试混合方法创建一个全面的数值数据库.
- 具体描述路面表面层之间的着层,这是近场GPR分析中的一个常见挑战.
主要方法:
- 使用gprMax开源软件模拟各种路面结构的地面透雷达 (GPR) 数据.
- 模拟基于法国标准的双层路面结构,不同的几何和物理参数的磨损过程,衣和粘合剂过程.
- 采用混合数据处理方法,将机器学习与全波形倒置集成,用于分析模拟的GPR数据.
主要成果:
- 成功生成了针对各种路面配置的模拟GPR响应的代表性数据库.
- 展示了混合机器学习和全波形反转方法在路面层的特征化方面的潜力.
- 解决了使用与地面合的GPR,甚至在具有构造干扰的情况下,区分薄青地下层的挑战.
结论:
- 开发的数值数据库支持先进的GPR数据处理技术的验证.
- 混合机器学习和全波形倒置方法显示了有效的路面地下表征的希望.
- 这项研究有助于更好地检测和了解影响路面性能的地下缺陷.
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