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相关概念视频

Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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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...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Toughness and Hardness of Aggregate01:22

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237
Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in...
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Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

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The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by...
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Elasticity in Concrete01:20

Elasticity in Concrete

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Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...
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Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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一种基于深度学习的岩石核心完整性的智能预测方法.

Zhaoxia Hu1, Hua Mei1, Lei Yu2

  • 1School of Civil and Architecture Engineering, Hunan Institute of Technology, Hengyang, 421002, China.

Scientific reports
|February 22, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习算法,岩心裂智能检测算法 (IDA-RCF),准确评估岩心完整性. 这种方法自动识别裂和完整性评估,改进了传统的手动评估.

关键词:
深度学习是一种深度学习.裂 裂是一种裂.地质技术工程地质技术工程智能预测智能预测岩石核心的完整性 岩石核心的完整性

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科学领域:

  • 地质技术工程 地质技术工程
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 传统的岩石核心完整性的手动评估是低效的,容易出现错误.
  • 对岩石核心完整性的准确评估对于地质和土木工程项目至关重要.

研究的目的:

  • 开发一种基于深度学习的算法,用于自动化岩石核心完整性评估.
  • 提高岩石核心裂识别和完整性评估的效率和准确性.

主要方法:

  • 拟议的IDA-RCF (岩芯裂智能检测算法) 使用双分支特征提取网络.
  • 采用局部裂特征的可变形卷积和对全球背景的自我注意的EfficientViT.
  • 实施了多层次的功能融合网络,以适应地整合本地和全球信息.

主要成果:

  • IDA-RCF实现了高准确性,F1得分为93.09%,mAP@0.5为94.44%,mAP@0.5:0.95为84.61%. 这两项测试都取得了很高的准确性.
  • 与手动方法相比,该算法在裂纹率预测中显示出4.38%的低相对误差.
  • 对于岩石核心完整度的预测准确度达到了93.8%.

结论:

  • 拟议的IDA-RCF算法为评估岩石核心完整性提供了一个精确而智能的解决方案.
  • 使用IDA-RCF的自动评估显著提高了手工方法的效率和可靠性.
  • 这种深度学习方法对推进岩石力学和地质技术工程应用有前途.