机场混凝土路面板块的空隙检测基于移动载荷下的振动响应
Xiang Wang1, Ziliang Ma1, Xing Hu1
1Department of Roadway Engineering, Southeast University, Nanjing 210000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种基于振动的方法,用于检测和测量机场混凝土路面角落下的空隙. 使用机器学习,该方法可以准确量化空隙大小,提高路面诊断.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 材料科学 材料科学 材料科学
背景情况:
- 机场混凝土路面下面的亚板空洞带来了重大的结构风险.
- 这些空洞的早期检测和量化对于路面维护和安全至关重要.
- 现有的检测方法可能缺乏精度或效率.
研究的目的:
- 开发和验证一种基于振动的方法,用于检测和量化机场混凝土路面中的亚板角空洞.
- 探索时间频域特征和机器学习对空格特征的有效性.
- 建立一种可靠的方法,以智能诊断混凝土路面的健康状况.
主要方法:
- 缩小尺寸的板块模型的建造,并进行移动负载模拟.
- 收集和分析加速信号以提取时间频率特征 (PSD,斜率,频率中心).
- 开发一个有限元模型来模拟路面在各种空隙条件下的反应.
- 应用一个随机森林 (RF) 模型,使用光谱能量指标和几何参数来估计空洞大小.
主要成果:
- 随机森林模型在估计空洞大小方面表现出强大的预测性能.
- 主要的光谱能量指标和几何参数与空洞特征有很高的相关性.
- 模拟,信号分析和机器学习的综合方法被证明是可行的.
结论:
- 基于振动的分析与机器学习相结合,为检测和量化亚板空洞提供了可行的解决方案.
- 这种方法支持智能诊断,以改善混凝土路面管理.
- 该研究强调了基础设施监控中先进计算技术的潜力.
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