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相关概念视频

Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

943
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 a...
943
Design Example: Joints in Concrete Pavements01:28

Design Example: Joints in Concrete Pavements

486
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...
486
Elasticity in Concrete01:20

Elasticity in Concrete

323
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...
323
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

785
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

501
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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Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

604
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...
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相关实验视频

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Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
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在FWD数据上使用时间结构深度模型和基于扰乱的评估来预测坚固的路面模块.

Xinyu Guo1, Yue Chen2, Nan Sun3

  • 1Faculty of Information Science and Technology, University Kembangan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括

这项研究引入了使用深度学习预测路面结构模块的新框架. 它强调了输入序列化策略如何显著影响模型准确性和对噪音数据的稳定性.

关键词:
在FWD中使用FWD.这是一个很好的选择 ResRNNNN深度学习是一种深度学习.路面模块预测路面模块的预测干扰强度 强度 干扰强度序列建模 序列建模

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 地质技术工程 地质技术工程
  • 数据科学数据科学数据科学

背景情况:

  • 准确的路面结构模块预测对于基础设施维护和生命周期评估至关重要.
  • 深度学习模型显示出有希望的结果,但与时间序列数据结构和测量噪声作斗争.

研究的目的:

  • 开发一个使用深度学习进行路面模块预测的综合框架.
  • 研究输入序列化策略对模型性能和稳定性的影响.
  • 在模拟的传感器不确定性下评估模型的可靠性.

主要方法:

  • 为时间序列数据开发了五种输入序列化策略 (A-E计划).
  • 使用混合宽深ResRNN架构 (SimpleRNN,GRU,LSTM) 进行多层模块预测.
  • 通过蒙特卡洛模拟注入高斯噪声 (±3%的差异),以评估稳定性和估计的置信区间.

主要成果:

  • 输入时间步骤设计极大地影响了预测的准确性和稳定性.
  • 计划D测序策略显示了准确性和稳定性之间的最佳平衡.
  • 拟议的框架有效处理杂数据,并提供置信区间.

结论:

  • 系统的时间步骤建模和基于扰动的评估增强了铺路工程的深度学习.
  • 该框架为在不确定的现场条件下预测路面模块提供了一个实用和可通用的解决方案.