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相关概念视频

Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

175
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...
175
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

213
Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
213
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

122
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...
122
Elasticity in Concrete01:20

Elasticity in Concrete

95
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...
95
Strength of Cement01:20

Strength of Cement

141
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...
141
Toughness and Hardness of Aggregate01:22

Toughness and Hardness of Aggregate

267
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...
267

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使用软计算技术估计混凝土材料的单轴压力强度.

Matiur Rahman Raju1, Mahfuzur Rahman1,2, Md Mehedi Hasan3

  • 1Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.

Heliyon
|November 30, 2023
PubMed
概括

卷积神经网络 (CNN) 最好预测混合设计的混凝土强度. 这项研究比较了CNN,封闭反复单位 (GRU) 和长期短期记忆 (LSTM) 网络,以预测混凝土的强度.

关键词:
进行比较分析.混凝土的压力强度是什么深度学习是一种深度学习.混合设计混合设计.模型优化模型优化

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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
  • 人工智能的人工智能

背景情况:

  • 准确的混凝土强度预测对于建筑安全和效率至关重要.
  • 现有研究经常在各种混凝土类型上使用各种机器学习算法.
  • 在具体比较混合设计混凝土强度的先进深度学习模型方面存在差距.

研究的目的:

  • 为了比较分析卷积神经网络 (CNN),门式循环单元 (GRU) 和长期短期记忆 (LSTM) 网络.
  • 确定最佳的深度学习 (DL) 算法来预测混合设计混凝土的单轴压力强度.
  • 为了提高建筑行业的材料属性预测.

主要方法:

  • 使用实验数据开发和优化深度学习模型 (CNN,GRU,LSTM).
  • 专注于混合设计的具体数据集.
  • 应用超参数调整和规范化技术用于模型改进.

主要成果:

  • 与GRU和LSTM模型相比,CNN模型在预测混凝土单轴压力方面表现出卓越的性能.
  • 优化的DL算法显示了增强的预测准确性.
  • 超参数调整和规范化进一步提高了模型性能.

结论:

  • CNNs是预测混合设计混凝土强度的最佳深度学习算法.
  • 这项研究为材料属性预测提供了实际解决方案,有可能提高建筑效率和质量.
  • 这些发现有助于减少建筑行业的资源负担.