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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

128
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...
128
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

168
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.
168
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

124
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...
124
Reinforcements in Concrete01:25

Reinforcements in Concrete

94
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
94
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

134
Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
The dimensions and shape of a concrete specimen...
134
Creep in Concrete01:22

Creep in Concrete

274
Creep refers to the time-dependent increase in strain under a sustained load, excluding other time-dependent deformations associated with shrinkage, swelling, and thermal expansion in concrete. The primary mechanism behind creep involves the loss of physically adsorbed water from the calcium silicate hydrate within the hydrated cement paste. This process is further exacerbated by concrete's non-linear stress-strain relationship, microcrack development in the interfacial transition zone, and...
274

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相关实验视频

Updated: Jul 12, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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基于值的BRISQUE辅助深度学习,用于增强混凝土结构中的裂纹检测.

Sanjeetha Pennada1, Marcus Perry1, Jack McAlorum1

  • 1Department of Civil and Environmental Engineering, University of Strathclyde, 75 Montrose St., Glasgow G1 1XJ, UK.

Journal of imaging
|October 27, 2023
PubMed
概括
此摘要是机器生成的。

图像质量显著影响使用深度学习的裂检测. 较低的BRISQUE分数表明更好的图像质量,导致更高的精度识别混凝土裂和降低计算成本.

关键词:
布里斯克 (Brisque) 是一个小镇.在VGG16中,VGG16是VGG16中的一个.二元分类是二元分类中的一种.混凝土裂检测,混凝土裂检测数据清理数据清理深度学习是一种深度学习.图像处理是图像处理的过程.图像质量评估 图像质量评估神经网络的神经网络的神经网络结构健康监测 结构健康监测

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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:

  • 混凝土结构的自动视觉检查系统依赖于精确的裂检测.
  • 卷积神经网络 (CNN) 的性能受到低质量的图像的阻碍.
  • 评估图像数据集的适用性对于可靠的深度学习模型至关重要.

研究的目的:

  • 评估图像退化 (高斯噪声,模糊) 对使用VGG16.16的裂纹检测的影响.
  • 探索图像质量指标 (BRISQUE) 和CNN性能之间的相关性.
  • 为降低计算成本提出优化培训和测试的方法.

主要方法:

  • 使用BRISQUE (盲人/无参考图像空间质量评估器) 方法来量化图像退化.
  • 在数据集上训练和评估VGG16模型,数据集具有不同级别的高斯噪声和模糊.
  • 分析了BRISQUE分数和裂分类指标 (精度,F1分数,MCC) 之间的关系.

主要成果:

  • 在较低的BRISQUE分数和改善的裂分类性能之间发现了强烈的相关性.
  • 更高的精度,F1得分和马修的相关系数 (MCC) 通过更少的图像降解来实现.
  • 实施BRISQUE分数门可以优化模型培训和测试.

结论:

  • 图像质量是CNN基于裂纹检测成功的一个关键因素.
  • 在破裂检测的背景下,BRISQUE是评估图像质量的敏感指标.
  • 使用BRISQUE值可以带来更高效和更具成本效益的结构健康监测的自动化视觉检查系统.