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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
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

181
Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
181
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
Creep in Concrete01:22

Creep in Concrete

223
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...
223
Frost Action on Concrete01:27

Frost Action on Concrete

94
Concrete structures in cold climates, such as those along roadsides, can retain moisture. This moisture makes them susceptible to frost-related damage when temperatures fall below freezing. Adding moisture worsens the damage during temperature fluctuations, leading to repeated freezing and thawing. De-icing salts, spread over these structures to melt ice, add to the freeze-thaw cycle, and draw even more moisture into the concrete.
This freeze-thaw cycle primarily causes surface scaling, where...
94
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

126
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...
126

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相关实验视频

Updated: Jun 25, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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深度CrackNet:一个深度学习模型,用于自动检测路面裂.

Alireza Saberironaghi1, Jing Ren1

  • 1Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada.

Journal of imaging
|May 24, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了DepthCrackNet,这是一种用于自动化路面裂检测的深度学习模型. 这种先进的系统通过有效地识别道路裂来提高道路安全,优于公开数据集上的以前方法.

关键词:
注意力机制注意力机制自动检测缺陷的自动检测.裂纹细分 裂纹细分 裂纹细分深度学习是一种深度学习.检测缺陷检测检测缺陷检测的方法功能提取 特性提取多头注意力多头注意力路面裂检测 路面裂检测表面缺陷检测检测表面缺陷检测

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Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method
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相关实验视频

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 道路基础设施维护 道路基础设施维护

背景情况:

  • 手动检测路面裂是劳动密集型和耗时的,阻碍了有效的道路安全管理.
  • 自动裂检测至关重要,但由于裂的变性,不同的路面材料和表面异常,它面临着挑战.

研究的目的:

  • 提出一个有效的深度学习模型,DepthCrackNet,用于自动化路面裂检测.
  • 提高道路基础设施裂纹细分的准确性和效率.

主要方法:

  • 开发了DepthCrackNet,这是一个U-Net形状的模型,包含一个双卷积编码器 (DCE) 以实现高效的特征提取.
  • 集成了TriInput多头空间注意 (TMSA) 模块,以捕捉各种空间关系和上下文信息.
  • 利用空间深度增强器 (SDE) 模块来增强功能提取功能,以改善细分.

主要成果:

  • 在Crack500数据集上,DepthCrackNet实现了平均77.0%的跨欧盟交叉点 (mIoU).
  • 该模型在DeepCrack数据集上获得了83.9%的mIoU,在裂检测方面表现出很高的性能.
  • 实验结果验证了该模型在准确细分路面裂方面的有效性.

结论:

  • DepthCrackNet提供了一种强大而高效的解决方案,用于自动检测路面裂.
  • 拟议的模型,其新的模块,显著提升了道路表面分析的最先进技术.
  • 这项技术有可能通过改进裂识别来提高道路安全和维护策略.