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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

110
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...
110
Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

101
Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
101
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

132
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.
132
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

113
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...
113
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.2K
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

179
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...
179

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相关实验视频

Updated: Jun 12, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

999

混凝土裂检测和分离:一个特征融合,裂隔离和可解释的基于人工智能的方法.

Reshma Ahmed Swarna1,2, Muhammad Minoar Hossain1,2, Mst Rokeya Khatun2

  • 1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh.

Journal of imaging
|September 27, 2024
PubMed
概括

这项研究介绍了一种用于混凝土裂检测的智能方案,该方案使用来自卷积神经网络和手工制作的方法的融合特征. 先进的方法实现了高准确性,并提供了解释,改善了结构安全评估.

关键词:
在 LDA LDA 中.凸凸的船体外.裂识别系统 裂识别系统曲线变换的曲线变换.可以解释的人工智能AI功能融合功能融合功能

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Last Updated: Jun 12, 2025

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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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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
  • 结构健康监测 结构健康监测

背景情况:

  • 目前基于图像的裂检测方法缺乏对各种条件的性能理解.
  • 挑战包括图像分辨率,细裂纹检测和区分裂纹类型.
  • 需要改进的算法来进行准确的结构评估.

研究的目的:

  • 开发一个智能系统来识别裂,并从图像中量化它们的百分比.
  • 将深度学习和手工制作的方法的功能融合在一起,以改进裂检测.
  • 整合可解释的AI,以提高结构分析的清晰度和信任度.

主要方法:

  • 使用ResNet-50 (卷积神经网络) 和曲线变换 (手工制作) 的特征融合.
  • 通过线性区分分析 (LDA) 的优化和使用 eXtreme梯度增强 (XGB) 的分类.
  • 结合图像值,形态运算和轮检测以量化裂的新型算法;整合LIME和Grad-CAM++以实现可解释性.

主要成果:

  • 在两个数据集上实现了99.93%和99.69%的准确性,超过了最先进的方法.
  • 开发了一种新的算法,用于隔离和量化裂区域.
  • 在裂检测准确性和可解释性方面表现出卓越的性能.

结论:

  • 拟议的方法提供了一种可靠的工具,用于实时检测混凝土结构中的裂.
  • 通过及时维护,提高结构安全,并通过准确的评估来促进维护.
  • 通过透明的AI决策,增加对工程实践的信任和采用.