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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

416
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
416

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相关实验视频

Updated: Jan 11, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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先进的信号分析模型用于桥梁甲板的内部缺陷映射,使用冲击回声场测试.

Avishkar Lamsal1, Biggyan Lamsal1, Bum-Jun Kim1

  • 1Department of Civil Engineering, The University of Texas at Arlington, Nedderman Hall, 416 Yates St, Arlington, TX 76019, USA.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

本研究引入了一种深度学习模型,以提高桥梁甲板内部缺陷检测,使用冲击回声测试. 先进的信号分析显著提高了识别结构问题的准确性.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.脱层是分层的方法.冲击回声回声的影响.

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相关实验视频

Last Updated: Jan 11, 2026

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05:30

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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
  • 非破坏性测试 不破坏性测试

背景情况:

  • 桥梁甲板是关键基础设施,需要定期评估条件.
  • 冲击回声测试是一种常见的非破坏性方法,用于检测内部缺陷.
  • 在现场检查中,信号噪声和数据变异性挑战了准确的缺陷识别.

研究的目的:

  • 开发一个先进的信号分析模型,以改善桥梁甲板内部缺陷的识别.
  • 通过使用深度学习技术来减轻信号噪声和波动.
  • 为了提高撞击回声现场测试数据中缺陷检测的准确性.

主要方法:

  • 实地测试是在使用自动检查系统在混凝土桥甲板上进行的.
  • 一个深度学习框架,特别是一个卷积神经网络 (CNN),用于信号分析.
  • 信号参数,如持续时间和零交叉起点,通过系统调来优化.

主要成果:

  • 确定了最佳信号参数 (1ms持续时间,0.1ms开始时间),实现了88.8%的分类准确性.
  • 与预优化相比,CNN优化参数显著提高了缺陷检测准确度.
  • 实验室测试验证了优化过程中观察到的信号行为趋势.

结论:

  • 先进的信号分析和深度学习与冲击回声测试的整合提供了一个强大的NDT方法.
  • 开发的模型有效地改进了信号参数,以便在桥甲板上准确识别内部缺陷.
  • 这种方法为大规模基础设施状况评估提供了有前途的解决方案.