Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

295
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.
295
Workability of Concrete01:25

Workability of Concrete

404
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...
404
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

497
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
497
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

13.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
13.8K
Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.3K
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Coal Gangue Recycling in Construction Materials: Strategies for Environmental Risk Mitigation via Heavy Metal Immobilization and Resource Utilization.

Materials (Basel, Switzerland)·2026
Same author

Synergistic Utilization of Multi-Source Industrial Solid Wastes in Cementitious Materials: A Comprehensive Review.

Materials (Basel, Switzerland)·2026
Same author

A Review of Steel Slag Carbonation: Mechanisms, Applications, and Sustainability Assessment.

Materials (Basel, Switzerland)·2026
Same author

One-Week Hydration Characteristics of Silica-Alumina Based Cementitious Materials Composed of Phosphorous Slag: Phosphorus Involved in Calcium Alumino-Silicate Hydrate Gel.

Materials (Basel, Switzerland)·2025
Same author

Comprehensive Utilization of Industry By-Products in Precast Concrete: A Critical Review from the Perspective of Physicochemical Characteristics of Solid Waste and Steam Curing Conditions.

Materials (Basel, Switzerland)·2024
Same author

The Utilization of Carbonated Steel Slag as a Supplementary Cementitious Material in Cement.

Materials (Basel, Switzerland)·2024

相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

可解释的机器学习用于多组件混凝土:预测建模和特征交互见解.

Jie Wang1, Junqi Deng1, Siyi Li1

  • 1School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Materials (Basel, Switzerland)
|October 16, 2025
PubMed
概括

机器学习模型通过分析关键因素来预测混凝土的压力强度. 这种数据驱动的方法增强了工程设计,并促进了可持续的建筑材料.

关键词:
互动互动互动互动互动.机器学习是机器学习.多元组件混凝土是多元组件混凝土.强度强度强度强度强度强度强度强度强度强度强度强度强

更多相关视频

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.7K

相关实验视频

Last Updated: Jan 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.7K

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.
  • 计算机科学 计算机科学

背景情况:

  • 多元组件的具体绩效评估依赖于主观的专家判断和漫长的监测.
  • 人工智能 (AI) 和机器学习 (ML) 为构建科学提供了先进的数据分析能力.
  • 解决来自人为因素的不确定性对于可靠的具体绩效评估至关重要.

研究的目的:

  • 通过使用各种机器学习技术,调查影响混凝土压力强度的关键因素.
  • 通过SHAP分析提高ML模型在具体科学中的可解释性.
  • 为优化具体性能和可持续性提供数据驱动的基础.

主要方法:

  • 应用各种机器学习算法:线性回归,多项式回归,决策树,随机森林,ExtraTrees,AdaBoost,CatBoost,XGBoost和TabPFN.
  • 使用SHAP (夏普利添加式扩展) 分析来发现特征的重要性和相互作用.
  • 数据驱动的调查对多元组件混凝土压力强度的影响因素.

主要成果:

  • 通过ML模型分析确定影响混凝土压力强度的关键因素.
  • 通过SHAP,更好地了解多元件混凝土系统中的特征重要性和相互作用.
  • 展示ML能够提供对具体表现的预测洞察力的能力.

结论:

  • 机器学习为评估混凝土压力强度提供了一个强大的,数据驱动的方法.
  • SHAP分析提供了有价值的解释性,揭示了具体材料科学的潜在机制.
  • 这些发现支持改进工程设计,建设决策和开发可持续的混凝土材料.