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相关概念视频

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

300
This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
300
Workability of Concrete01:25

Workability of Concrete

419
The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
Concrete's workability is determined by its resistance to internal forces that arise...
419
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

518
Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
518
Additives and Fillers in Concrete01:29

Additives and Fillers in Concrete

332
Additives and fillers are integral to enhancing the properties of concrete. Pozzolans and blast-furnace slag are additives or admixtures due to their reactions with calcium hydroxide released during cement hydration. Fillers, which are finely ground and similar in fineness to Portland cement, improve concrete attributes such as workability density, and reduce capillary bleeding or cracking. Some fillers possess hydraulic properties or participate in benign reactions within the cement paste.
The...
332
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

939
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...
939
Elasticity in Concrete01:20

Elasticity in Concrete

322
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...
322

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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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基于可解释机器学习的纳米改造混凝土的多目标优化.

Yue Gu1, Ruyan Fan1, Yikun Li2

  • 1College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China.

Nanomaterials (Basel, Switzerland)
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测了纳米改性混凝土 (NSC) 的强度. 使用NSGA-II的优化框架平衡了可持续混凝土混合设计的强度,成本和碳排放.

关键词:
这是NSGA-II算法.在XGBoost算法中,我们使用了XGBoost算法.混凝土混凝土混凝土是什么机器学习是机器学习.多目标优化多目标优化

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科学领域:

  • 材料科学 材料科学 材料科学
  • 土木工程 土木工程是指土木工程.
  • 计算科学 计算科学

背景情况:

  • 纳米改性混凝土 (NSC) 在现代工程中至关重要.
  • 对于NSC来说,传统的混合比例设计是低效的,需要大量的时间和资源.

研究的目的:

  • 开发精确的机器学习模型来预测NSC压力强度.
  • 为可持续的NSC混合设计建立一个多目标优化框架,考虑强度,成本和碳足迹.

主要方法:

  • 四个机器学习模型 (XGBoost,CatBoost,随机森林,AdaBoost) 被训练来预测压力强度.
  • 用NSGA-II算法基于表现最佳的模型进行多目标优化.
  • 进行了特征重要性分析,以确定关键影响因素.

主要成果:

  • XGBoost 显示出优异的预测准确性 (R2 = 0.99,RMSE = 1.80 MPa).
  • 纳米含量显著影响强度 (0.82) 和成本 (0.85).
  • NSGA-II算法产生了帕雷托最佳解决方案,说明了压力强度,成本和碳排放之间的权衡.

结论:

  • 综合机器学习和优化方法提高了NSC混合比例设计的效率.
  • 这种方法为开发可持续且具有成本效益的具体配方提供了有价值的参考.
  • 该研究成功地减少了实验工作量,同时促进了环保工程实践.