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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

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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...
111

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相关实验视频

Updated: Jun 17, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

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在结构中使用声波形和深度学习的自动裂识别.

Mohamed Barbosh1, Liangfu Ge1, Ayan Sadhu1

  • 1Department of Civil and Environmental Engineering, The Western Academy for Advanced Research, Western University, London, ON N6A 3K7 Canada.

Journal of infrastructure preservation and resilience
|August 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种深度学习模型,使用声辐射 (AE) 波形来预测结构损坏的严重程度和位置. 这种新的方法自动化了分析,在识别结构元素损坏时实现了高精度.

关键词:
在AEAEAEAEAEAEAEAEAEAE具体的元素是具体的元素.深度学习是一种深度学习.损害的定位 损害的定位预测损坏严重程度的预测.

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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相关实验视频

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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
  • 人工智能的人工智能

背景情况:

  • 结构元素容易受到环境因素和关键负载的损害.
  • 声辐射 (AE) 波形分析对于检测微裂至关重要,但通常依赖于主观特征选择.
  • 在结构中自动化损坏评估对于安全和维护至关重要.

研究的目的:

  • 开发和验证一种深度学习模型,用于自动预测结构元素中的损坏严重程度和位置.
  • 为了提高结构健康监测,利用声辐射 (AE) 波形.
  • 克服传统的主观AE分析方法的局限性.

主要方法:

  • 密集连接的卷积神经网络 (CNN) 用于从时间域AE波形中提取高级特征.
  • 深度学习模型是使用混凝土和木结构元素的AE数据进行训练和验证的.
  • 该模型直接处理原始AE波形,消除了手动特征工程的需要.

主要成果:

  • 提出的深度学习模型准确地预测了损害严重程度,准确度为92-95%.
  • 该模型在识别测试结构元素中损坏的近似位置时达到90-100%的准确性.
  • 在混凝土和木质梁和板上的验证证明了该方法的稳定性.

结论:

  • 开发的深度学习方法提供了一种强大的自动化技术,用于预测和定位在民用结构中的损害严重程度.
  • 这种方法通过提供客观和准确的损害评估来增强结构健康监测.
  • 基于CNN的模型显示了基础设施安全和维护的真实应用的巨大潜力.