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相关概念视频

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

267
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
267
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

332
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...
332
Hooke's Law01:26

Hooke's Law

388
Hooke's law, a pivotal principle in material science, establishes that the strain a material undergoes is directly proportional to the applied stress, defined by a factor called the modulus of elasticity or Young's modulus.
388
Elasticity in Concrete01:20

Elasticity in Concrete

94
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...
94
Strain-Energy Density01:20

Strain-Energy Density

410
Understanding the strain energy density in materials under axial load is crucial for evaluating their mechanical behavior and durability. When a rod is subjected to such a load, it elongates and stores energy, known as strain energy, as potential energy within the material. This energy is measured in terms of energy per unit volume.
In the elastic region of a material, the relationship between the stress and the strain is linear and follows Hooke's Law. The strain energy density in this...
410
Bending of Members Made of Several Materials01:08

Bending of Members Made of Several Materials

149
In analyzing a structural member composed of two different materials with identical cross-sectional areas, it is crucial to understand how their distinct elastic properties affect the member's response under load. The analysis involves assessing stress and strain distributions using the transformed section concept, which accounts for variations in material properties.
Hooke's Law determines stress in each material, stating that stress is proportional to strain but varies due to each...
149

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相关实验视频

Updated: Jul 4, 2025

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

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预测弹性模块:一个数据驱动的方法,整合物理和数值技术.

Kashif Riaz1, Naveed Ahmad1

  • 1University of Engineering & Technology, Taxila, Pakistan.

Heliyon
|February 8, 2024
PubMed
概括
此摘要是机器生成的。

这项研究有效地使用人工神经网络 (ANN) 和超声波脉冲速度 (UPV) 方法预测路面的次级质量. 这些技术为传统的,耗时的弹性模块 (MR) 测试提供了切实可行的替代方案.

关键词:
人工神经网络的人工神经网络循环三轴压缩循环三轴压缩弹性模块 弹性模块 弹性模块 弹性模块超声波脉冲速度的超声波脉冲速度是什么

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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

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Studying Large Amplitude Oscillatory Shear Response of Soft Materials
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Studying Large Amplitude Oscillatory Shear Response of Soft Materials

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相关实验视频

Last Updated: Jul 4, 2025

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科学领域:

  • 地质技术工程 地质技术工程
  • 材料科学 材料科学 材料科学

背景情况:

  • 弹性模块 (MR) 对于路面设计至关重要,评估次级材料质量.
  • 由于成本,时间和设备的限制,MR的实验性确定往往是不切实际的.

研究的目的:

  • 通过实验性超声波脉冲速度 (UPV) 和循环三轴测试来确定MR值.
  • 开发和验证一个人工神经网络 (ANN) 模型来预测MR.

主要方法:

  • 收集了24个土壤样本 (粗粒和细粒) 用于实验分析.
  • 利用阿特伯格极限和压缩属性作为ANN建模的输入变量.
  • 通过UPV和循环三轴测试对实验MR值进行验证的ANN预测.

主要成果:

  • 循环三轴测试产生了MR值,比UPV测试高约5%.
  • ANN和UPV方法与MR的循环三轴测试结果有很强的一致性.
  • ANN建模为预测弹性模块提供了一种高效的方法.

结论:

  • UPV和ANN技术为MR的确定提供了可行的,高效的替代方案.
  • 开发的ANN模型在预测弹性模块方面表现出显著的准确性.
  • 这项研究推进了用于铺路次级特征的高效方法.