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相关概念视频

Structural Classification of Joints01:20

Structural Classification of Joints

4.3K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Reinforced Brick Masonry01:15

Reinforced Brick Masonry

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Reinforced brick masonry is an advanced construction technique that enhances the structural integrity of brick walls by incorporating steel reinforcements. These reinforcements are either placed within the hollow cores of bricks or sandwiched between two layers of masonry, known as wythes, and are then secured in place with grout. Grout is a fluid mixture composed of Portland cement, aggregate, and water, providing the necessary bonding agent for the steel and brick.
To fortify brick walls...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
166
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

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When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal...
1.4K
Masonry Loadbearing Walls01:16

Masonry Loadbearing Walls

161
Masonry load-bearing walls, constructed from materials like brick, stone, or concrete masonry units, serve as a crucial component in building structures by supporting the loads from floors and roofs and transferring them to the foundation. These walls, known for their compressive strength, can be reinforced or unreinforced to suit different building needs, accommodating both the dead and live loads while maintaining safety through lower working stresses compared to the materials' ultimate...
161
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

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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:
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Updated: Sep 17, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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使用机器学习和整体特征选择技术预测建筑物的可拆解性.

Habeeb Balogun1,2, Hafiz Alaka3, Eren Demir3

  • 1Big Data Technologies and Innovation Lab., University of Hertfordshire, Hatfield, United Kingdom. h.balogun@herts.ac.uk.

Scientific reports
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

一个新的机器学习模型预测了建筑物的解构潜力,促进了建筑中的循环经济原则. 这种方法简化了废物减少,提高了英国和全球的资源效率.

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科学领域:

  • 建设管理建设管理.
  • 可持续工程 可持续工程
  • 在土木工程中的人工智能.

背景情况:

  • 建筑行业是全球主要的资源消费者和废物产生者.
  • 循环经济原则正在获得引力,以提高资源效率,并通过物质再利用释放经济价值.
  • 建筑物解体,即仔细拆卸以重复使用组件,符合循环经济的目标,但需要有效的评估.

研究的目的:

  • 解决手动建筑拆除评估 (耗时和昂贵) 的局限性.
  • 开发一种基于机器学习的预测模型,用于评估建筑物的可拆解性.
  • 在现实世界解构项目中展示开发模型的实际应用.

主要方法:

  • 开发一种专门用于预测建筑物的可拆解性而设计的机器学习模型.
  • 利用整体特征选择技术来识别影响解构潜力的关键因素.
  • 通过在建筑拆除项目的案例研究中应用该模型的性能验证.

主要成果:

  • 通过机器学习成功创建了可解构性预测模型.
  • 在实际的解构场景中证明模型的适用性和潜力.
  • 该模型为传统的手动检查方法提供了更有效的替代方案.

结论:

  • 开发的机器学习模型为评估建筑物的可拆解性提供了有效的解决方案.
  • 这种预测工具支持建筑行业采用循环经济战略.
  • 这项研究有助于提高资源效率,使建筑物在使用寿命结束时的经济价值恢复.