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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

116
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...
116
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

147
Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
147

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相关实验视频

Updated: Jun 25, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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通过混合视觉变压器算法改进混凝土裂检测过程.

Mohammad Shahin1, F Frank Chen1, Mazdak Maghanaki1

  • 1Mechanical Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249, USA.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括

这项研究使用计算机视觉改进了混凝土桥梁检查. 一个定制的卷积神经网络 (CNN) 在裂检测方面实现了超过99%的准确性,在效率方面超过视觉变压器 (ViT).

关键词:
工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是一个大数据就是大数据.基于计算机的视觉视觉.混凝土裂检测,混凝土裂检测检查 检查 检查 检查 检查机器学习是机器学习.维护维护维护维护维护维护维护减少废物 减少废物

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 每两年对美国老旧的混凝土桥梁进行检查需要大量的资源.
  • 传统的裂检测方法耗时且昂贵.
  • 对于高效的桥梁检查技术有着至关重要的需求.

研究的目的:

  • 评估计算机视觉模型,以提高混凝土桥梁检查效率.
  • 为了比较视觉变压器 (ViT) 和卷积神经网络 (CNN) 模型的裂纹检测性能.
  • 评估图像增强算法对检测准确性的影响.

主要方法:

  • 应用最先进的视觉变压器 (ViT) 和卷积神经网络 (CNN) 模型.
  • 利用了来自Concrete Images for Classification数据集的超过2万张高质量的图像.
  • 集成ViT与各种图像增强检测器算法进行基准测试.

主要成果:

  • 一个定制的CNN模型在混凝土裂检测中实现了99%以上的准确性.
  • 与ViT模型相比,CNN模型的训练时间显著减少.
  • 与图像增强探测器相结合的ViT显示了更好的混凝土裂检测精度.

结论:

  • 计算机视觉,特别是CNN,为混凝土桥梁裂检测提供了有效的解决方案.
  • 这些进步提高了基础设施安全,支持资源节约,并与工业4.0自动化目标保持一致.
  • 自动化手动检查降低了成本,并将技术整合到公共基础设施管理中.