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相关概念视频

Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

508
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...
508
Aggregate Cement Ratio01:21

Aggregate Cement Ratio

535
The Aggregate Cement ratio refers to the weight of aggregate divided by the weight of cement in a concrete mix. Altering this ratio has profound effects on the concrete's properties. This ratio plays a pivotal role in determining the strength, workability, and durability of concrete. When the Aggregate Cement ratio is higher, the mix is leaner, meaning it has less cement paste to lubricate the aggregate, potentially making the concrete less workable. Such mixes, known as lean, enhance the...
535
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
Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

531
Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
Another type of impurity is clay and fine material that...
531
Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

462
The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
462
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

535
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
535

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相关实验视频

Updated: Jan 14, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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使用优化机器学习模型预测回收聚合混凝土的自我愈合效率.

Kunpeng Cao1,2, Dunwen Liu3, Kian Hau Kong2

  • 1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Scientific reports
|October 22, 2025
PubMed
概括

这项研究使用机器学习来预测回收粗聚合物 (RCA) 的自愈混凝土性能. 一个优化的NRBO-XGBoost模型准确地预测了愈合率,显示裂宽度是关键,而不是RCA量.

关键词:
裂纹修复 裂纹修复机器学习 机器学习预测 预测 预测回收的聚合物聚合物回收.自愈的混凝土可以自我愈合.

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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.
  • 可持续建筑 可持续建筑

背景情况:

  • 自愈混凝土提供了增强的耐用性,但面临成本和复杂性的挑战.
  • 回收粗聚合物 (RCA) 是一种可持续的替代品,有可能降低成本并改善混凝土的性能.
  • 预测结合RCA的混凝土的自我愈合性能对于其实际应用至关重要.

研究的目的:

  • 开发和验证一个优化的机器学习 (ML) 模型,用于预测含有RCA的混凝土的自我修复性能.
  • 评估包括RCA含量在内的各种因素对混凝土自愈速率的影响.
  • 为评估可持续混凝土材料提供具有成本效益和效率的方法.

主要方法:

  • 编制了173个数据集的数据库,其中有8个输入变量和自我修复率作为输出.
  • 开发了一个优化的NRBO-XGBoost模型,并与其他四个ML模型和两个优化技术进行了比较.
  • 用沙普利方法进行灵敏度分析,以确定关键影响因素.

主要成果:

  • 优化的NRBO-XGBoost模型表现出卓越的性能,实现了高精度 (R2 = 0.9569,RMSE = 7.1800,MAE = 4.9575).
  • 灵敏度分析显示,裂宽度是影响自我愈合的最重要因素,RCA在测试范围内的影响最小.
  • 该模型的预测能力为评估自愈合混凝土性能提供了可靠的工具.

结论:

  • 该研究成功地引入了一种优化的ML方法,用于预测混凝土与RCA的自我愈合性能.
  • 虽然RCA在研究范围内对愈合的影响最小,但其经济和环境优势对于可持续建筑是显著的.
  • 这些发现提供了宝贵的理论见解和实际指导,用于利用回收材料在自愈的混凝土应用中.