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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

117
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...
117
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

154
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.
154
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

129
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...
129
Creep in Concrete01:22

Creep in Concrete

229
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...
229
Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

162
Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
162
Shrinkage in Concrete01:27

Shrinkage in Concrete

97
Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
97

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相关实验视频

Updated: Jun 30, 2025

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

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一种改进的基于变压器的混凝土裂分类方法.

Guanting Ye1,2, Wei Dai2, Jintai Tao2

  • 1College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, 830052, China.

Scientific reports
|March 15, 2024
PubMed
概括

一个新的Cross Swin变压器-skip (CSW-S) 模型增强了混凝土裂的识别. 这种高效的方法达到96.92%的准确性,超过了结构耐久性评估的传统模型.

关键词:
裂纹检测 裂纹检测 裂纹检测 裂纹检测深度学习是一种深度学习.图像特征提取 图像特征提取结构性健康监测 结构性健康监测变压器变压器变压器

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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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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:

  • 混凝土结构中的表面裂是耐用性和可用性的关键指标.
  • 目前用于裂识别的卷积神经网络 (CNN) 在计算上昂贵且低效.

研究的目的:

  • 为准确和高效的混凝土裂分类提出一个优化的Cross Swin变压器-skip (CSW-S) 网络.
  • 改进现有的变压器和CNN模型用于裂检测.

主要方法:

  • 通过将剩余链接纳入Cross Swin变压器架构,开发了一个新的CSW-S网络.
  • 在一个包含 17,000 个混凝土裂图像的数据集上训练和评估了 CSW-S 模型.

主要成果:

  • 增强的CSW-S网络展示了提取图像特征的能力提高,导致更高的裂识别精度.
  • 在没有预训练的情况下,实现了96.92%的检测准确度.
  • 与传统的变压器和CNN模型相比,CSW-S模型表现出更高的识别效率和准确性.

结论:

  • 优化的CSW-S网络为混凝土裂分类提供了更准确,更有效的解决方案.
  • 这一进步有助于在土木工程应用中更好的结构健康监测和耐久性评估.