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相关概念视频

Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

542
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...
542
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...
137
Strength of Cement01:20

Strength of Cement

214
Strength tests for cement are not performed directly on neat cement paste due to difficulty in obtaining consistent, reliable specimens. Instead, cement is typically tested in the form of cement-sand mortar.
For compressive strength tests, ASTM C 109-05 standards prescribe a cement-sand mix ratio of 1:2.75 and a water/cement ratio of 0.485 for making 2-inch cubes. These cubes are mixed, cast, and cured in saturated lime water at 23°C until testing. Flexural strength testing, outlined in...
214
Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

273
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...
273
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

327
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.
327
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

215
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
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一个深度学习模型用于预测水泥土的变形模块.

Feng Zheyuan1, Chen Cheng2, Dong Manman3

  • 1State Key Laboratory of Disaster Prevention & Mitigation of Explosion & Impact, Army Engineering University of PLA, Nanjing, Jiangsu 210007, China.

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概括
此摘要是机器生成的。

这项研究引入了一种改进的卷积长期短期记忆 (ConvLSTM) 模型,用于预测道填充中的水泥弹性模量. ConvLSTM模型显示出比传统方法更高的准确性,特别是在大型数据集.

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 水泥对于屏蔽道的稳定性至关重要,但预测其性能,特别是弹性模量 (E50) 是一个挑战.
  • 准确预测水泥性能对于确保道结构的长期完整性和安全性至关重要.

研究的目的:

  • 开发和评估一种新的机器学习模型,即改进的卷积长期短期记忆 (ConvLSTM),用于预测道填充中使用的水泥的弹性模量 (E50).
  • 识别和排名影响水泥弹性模量的主要因素.

主要方法:

  • 使用了改进的卷积长期短期记忆 (ConvLSTM) 模型,该模型包含了用于参数重要性差异化的道注意力和用于捕获信息的注意力机制.
  • 使用最大信息系数算法来确定各种因素与E50.5之间的相关性.
  • 通过使用不同数据量,比较ConvLSTM与随机森林,支持向量回归和长短期记忆 (LSTM) 等传统模型的性能.

主要成果:

  • 最大信息系数算法确定了强度,水泥含量,土含量和固化时间是对E50.5最有影响力的因素.
  • ConvLSTM和LSTM模型在增加数据量时显示出更好的性能,在更大的数据集上表现优于随机森林和支持向量回归.
  • 拟议的ConvLSTM模型与传统的理论模型相比,实现了更高的预测准确性,并在不同材料中展示了强大的概括能力.

结论:

  • 改进的ConvLSTM模型提供了一种高度准确和可靠的方法,用于预测盾道应用中的水泥弹性模量.
  • 这些发现为影响水泥性能的关键参数提供了宝贵的见解,有助于道工程的材料选择和混合设计.
  • ConvLSTM模型的卓越性能突显了先进的机器学习技术在地质工程和材料科学中的潜力.