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相关概念视频

Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

279
Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
279
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

549
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...
549
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

356
Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
356
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

204
Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
The dimensions and shape of a concrete specimen...
204
Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

211
The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
211
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

209
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...
209

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使用基于BOIvy优化算法的机器学习模型来预测本托尼特塑料混凝土的压力强度.

Shuai Huang1, Chuanqi Li1, Jian Zhou1

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

Materials (Basel, Switzerland)
|July 12, 2025
PubMed
概括

机器学习模型准确地预测了托尼特塑料混凝土 (BPC) 的压力强度 (CS). 优化的贝叶斯常春藤-人工神经网络 (ANN) 模型显示出卓越的性能,识别了水和固化时间等关键因素.

关键词:
贝叶斯的优化是贝叶斯的优化.常春藤的算法 常春藤的算法班托尼特塑料混凝土是混凝土中的一种.压力强度的压力强度是什么模型的解释性可解释性

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 土木工程 土木工程是指土木工程.
  • 材料中的人工智能

背景情况:

  • 班托尼特塑料混凝土 (BPC) 提供结构和重金属吸附的好处.
  • 准确的压力强度 (CS) 预测对于BPC设计至关重要.
  • 传统的CS测试方法是耗时的,昂贵的和不确定的.

研究的目的:

  • 开发和评估用于预测BPC压力强度 (CS) 的机器学习 (ML) 模型.
  • 使用元启发式优化增强ML模型预测准确度.
  • 确定影响 BPC CS. 的关键因素.

主要方法:

  • 使用了包括支持向量回归 (SVR),人工神经网络 (ANN) 和随机森林 (RF) 在内的机器学习模型.
  • 与贝叶斯优化 (BOIvy) 集成的Ivy算法被用于优化ML模型.
  • 使用统计指数 (R2,RMSE,U1,U2,VAF) 和可解释性方法 (SHAP,灵敏度分析) 评估性能.

主要成果:

  • 博伊维-安恩模型表现出优异的预测性能,具有最佳的统计指数.
  • 水含量,固化时间和水泥被确定为对CS预测最有影响的因素.
  • SHAP和灵敏度分析为模型解释性提供了洞察力.

结论:

  • 优化的机器学习模型,特别是BOIvy-ANN,为BPC CS预测提供了传统方法的可靠替代方案.
  • 该研究强调了人工智能技术在评估先进建筑材料性能方面的潜力.
  • 了解影响因素有助于BPC的高效设计和应用.