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相关概念视频

Porosity in Cement Paste01:18

Porosity in Cement Paste

228
The porosity of concrete is a measure of the void spaces within its structure. These spaces impact its strength and durability significantly. When water and cement interact, a chemical reaction called hydration creates a semi-solid paste. This paste includes combined water, making up approximately 23% of the cement's dry mass, and gel water, which fills minuscule voids known as gel pores, accounting for about 28% of the cement gel volume.
The balance of water to cement in the mix is...
228
Porosity and Absorption of Aggregate01:20

Porosity and Absorption of Aggregate

403
Aggregates contain pores of varying sizes; while some are completely enclosed within the particles, others open onto the surface, allowing water to penetrate. The porosity of aggregates is a major factor contributing to the overall porosity of concrete, given that aggregates constitute about three-quarters of concrete's volume.
When all pores in an aggregate are filled with water, the aggregate is considered saturated and surface-dry. If left in dry air, water will evaporate until the...
403
Pore Size Distribution01:23

Pore Size Distribution

217
In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
Adequate...
217

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Updated: Sep 12, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
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用可解释的机器学习预测结造中的孔隙性.

Rafael Gaspar Bessa de Oliveira1, Jones Yudi1, Edson Paulo da Silva1

  • 1College of Technology, Department of Mechanical Engineering, University of Brasília, Federal District, Brasília 70910-900, Brazil.

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概括
此摘要是机器生成的。

机器学习模型准确地预测了结造材料中的多孔性. CatBoost获得了最好的结果,固体负载被确定为影响毛孔性的关键因素.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 制造业 工程 制造工程
  • 计算材料科学科学 计算材料科学

背景情况:

  • 结造是创建具有可调节性质的多孔材料的关键方法.
  • 从工艺参数预测孔隙性是复杂的,对材料设计至关重要.
  • 现有的方法缺乏优化结造材料所需的精度.

研究的目的:

  • 开发和评估机器学习模型,用于预测冷成型中的多孔性.
  • 使用可解释的人工智能识别影响毛孔度的关键过程参数.
  • 为了提高冷造材料的设计和优化.

主要方法:

  • 利用来自252篇关于陶,聚合物和复合材料的研究论文的实验数据.
  • 应用机器学习算法:CatBoost,随机森林和XGBoost.
  • 为了模型的可解释性,使用了沙普利增量解释 (SHAP).

主要成果:

  • 在测试组中,CatBoost模型实现了最高的预测准确性,测试组的R2为0.81.
  • SHAP分析发现固体负载是最有影响力的参数.
  • 较低的固体负载与更高的预测孔隙度相关,与理论预期保持一致.

结论:

  • 机器学习,特别是CatBoost,提供了一种强大的工具,用于预测冷成型中的多孔性.
  • 可解释人工智能 (SHAP) 提供了对材料行为和参数影响的关键见解.
  • 这种方法可以指导实验设计,并优化特定应用的材料特性.