多NDE技术方法以改进基于电阻力测量的混凝土桥甲板腐蚀的解释
Mustafa Khudhair1, Nenad Gucunski1
1Department of Civil & Environmental Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
机器学习通过整合来自多种非破坏性评估 (NDE) 技术的数据来增强混凝土桥甲板的电阻 (ER) 解释,改进结构耐用性评估.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 混凝土桥甲板容易降解.
- 精确评估混凝土结构对于安全性和寿命至关重要.
- 目前用于解释电阻 (ER) 数据的方法存在局限性.
研究的目的:
- 为了更好地解释电阻 (ER) 结果,混凝土桥梁甲板.
- 为了利用机器学习 (ML) 算法进行增强的NDE数据分析.
- 整合来自多种NDE技术的数据,以进行更可靠的评估.
主要方法:
- 使用数值模拟进行了参数研究,以分析影响ER测量的因素 (和,腐蚀,分层,混凝土覆盖,水分).
- 使用模拟数据开发了一个随机森林机器学习算法.
- 将ML算法应用于使用BEAST®设施的实际桥梁甲板的ER数据,并结合了其他NDE方法的数据.
主要成果:
- 该研究确定了各种参数对ER测量的影响.
- 开发了一个强大的ML模型来解释ER数据.
- 多种NDE技术的整合显著提高了ER解释准确度.
结论:
- 机器学习,特别是随机森林,为解释混凝土桥甲板中的ER数据提供了一个强大的工具.
- 通过ML将ER数据与其他NDE技术结合起来,可以显著提高结构评估的可靠性.
- 这种方法增强了对结构元件耐用性和安全性的评估.
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