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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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Measurement of Air Content in Concrete01:23

Measurement of Air Content in Concrete

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Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Microcracking in Concrete01:20

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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开箱机器学习模型用于使用XAI预测混凝土强度.

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此摘要是机器生成的。

预测混凝土的强度对于建设至关重要. XGBoost机器学习模型实现了最佳性能,为工程师提供了洞察力,以优化混凝土混合设计和施工实践.

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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
  • 数据科学数据科学数据科学

背景情况:

  • 高性能混凝土对于承受重负荷和极端天气的耐用基础设施至关重要.
  • 准确的混凝土强度预测是优化施工性能,成本和安全的关键.
  • 机器学习 (ML) 为结构工程挑战提供先进的解决方案,例如混凝土强度预测.

研究的目的:

  • 评估和比较八个流行的机器学习模型的性能,以预测混凝土的强度.
  • 根据混合物设计和加载条件确定最有效的ML算法来预测混凝土强度.
  • 为土木工程师提供可操作的见解,使用ML用于具体的应用.

主要方法:

  • 评估了八个ML模型:线性,,LASSO,决策树,随机森林,XGBoost,SVM和ANN.
  • 使用1030个具体样本的标准数据集进行模型训练和测试.
  • 为了模型的可解释性,使用了SHAP (夏普利添加式解释).

主要成果:

  • 一个集体学习技术XGBoost表现出卓越的性能.
  • 通过XGBoost模型实现了0.91的R-Square (R2) 和4.37的根平均平方误差 (RMSE).
  • SHAP分析为XGBoost模型提供了有关特征重要性的见解.

结论:

  • 集体学习方法,特别是XGBoost,对于具体的强度预测非常有效.
  • 该研究为优化混凝土混合设计和施工实践提供了有价值的数据驱动的见解.
  • 像XGBoost这样的ML模型可以显著提高土木工程项目的决策.