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相关概念视频

Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

492
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
492
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...
437

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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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实时检测混凝土结构中的缺陷,使用基于注意力的深度学习和GPR成像.

Jia-Yu Zhang1, Liang Huang2, Yu-Jian Guan3

  • 1School of Civil Engineering, Zhengzhou University, Zhengzhou, 450001, China.

Scientific reports
|October 10, 2025
PubMed
概括

这项研究引入了一种增强的YOLOv5模型,具有高效通道注意力 (ECA),用于改进地面透雷达 (GPR) 在混凝土中的缺陷检测. 该方法实现了基础设施健康监测的更高准确性和实时效率.

关键词:
注意力机制注意力机制在混凝土中检测缺陷.地面穿透雷达 (GPR) 是一种地面穿透雷达.无人驾驶飞行器是一种无人驾驶飞行器.这是一个YOLO YOLO.

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 人工智能的人工智能
  • 非破坏性测试 不破坏性测试

背景情况:

  • 在混凝土结构中,地下缺陷检测面临着准确性和实时效率的挑战.
  • 对地面透雷达 (GPR) 数据的自动化分析对于基础设施健康监测至关重要.
  • 缺陷数据集中的类不平衡阻碍了模型性能.

研究的目的:

  • 提高在混凝土结构中自动化GPR缺陷检测的准确性和实时效率.
  • 开发一个强大的深度学习模型用于地下缺陷识别.
  • 提高民用基础设施非破坏性测试的可靠性.

主要方法:

  • 建议采用一个增强的YOLOv5模型,与高效通道注意力 (ECA) 机制集成.
  • 一个深度卷积生成对抗网络 (DCGAN) 用于数据增强,以解决阶级不平衡.
  • 为培训和验证,专门编制了一套具体缺陷的专用数据集.

主要成果:

  • 与基线和其他注意力变体相比,YOLOv5+ECA模型实现了最高的平均平均精度 (mAP).
  • 拟议的模型保持了实时推断速度,适合无人机部署.
  • 通过ECA对特定频道的特征重新校准,显著提高了检测准确度.

结论:

  • 增强的YOLOv5+ECA模型为混凝土地下缺陷检测提供了精确有效的解决方案.
  • 这种方法推进了用于基础设施健康监测的自动化GPR分析.
  • 该方法适用于关键的混凝土结构,如道层和桥梁甲板.