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相关概念视频

Stress: General Loading Conditions01:15

Stress: General Loading Conditions

519
To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
519
Applications of Stress01:04

Applications of Stress

627
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
627
Principal Stresses01:24

Principal Stresses

728
The graphical depiction of normal and shearing stress equations is represented by a circle, demonstrating the interplay between these stresses under different angular conditions. The center of this circle C, located on the vertical axis, represents the average normal stress, while its radius shows the range of stress variations. At points A and B, where the circle intersects the horizontal axis, the maximum and minimum normal stresses are observed, occurring without shearing stress. These...
728
Components of Stress01:23

Components of Stress

478
Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
Interestingly, the hidden cube faces also experience these stresses, equal and...
478
General State of Stress01:21

General State of Stress

583
The general state of stress within a material can be accurately depicted using a stress tensor. This tensor encapsulates the internal forces distributed within a material subjected to external forces or deformations.
Specifically, consider a tetrahedral element where one face, labeled XYZ, is perpendicular to the line OA, and the remaining faces align with the coordinate axes with point O as the origin. At any point, such as point O, the stress tensor can be used to determine the stress...
583
Stresses under Combined Loadings01:23

Stresses under Combined Loadings

434
When analyzing a bent tube with a circular cross-section subjected to multiple forces, it is crucial to determine the stress distribution in order to maintain structural integrity under varied load conditions.
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...
434

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Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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基于机器学习的方法,用于分析船舶压力分布.

Bowen Jin1, Ji Zeng2, Liuqi Xu3

  • 1College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.

Scientific reports
|November 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种机器学习方法,使用压力传感器分析船舶应力分布. 该方法有效预测压力,确定用于增强结构健康监测 (SHM) 和运营安全的关键传感器.

关键词:
起重机船 起重机船是一艘起重机船.机器学习是机器学习.多层感知器多层感知器压力分布 压力分布在XGBoost中使用.

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科学领域:

  • 海军建筑和海洋工程
  • 计算科学 计算科学
  • 数据科学数据科学数据科学

背景情况:

  • 船舶结构健康监测 (SHM) 对运营安全至关重要.
  • 通过压力传感器分析应力分布,可以提高SHM性能,防止故障.
  • 了解应力数据是提高船舶完整性的关键.

研究的目的:

  • 开发一种机器学习计算方法,用于分析起重机船压力分布.
  • 使用来自多个压力传感器的数据推断应力值.
  • 识别关键传感器,以有效监测压力.

主要方法:

  • 使用极端梯度增强 (XGBoost) 来评估压力传感器数据之间的关系.
  • 采用多层感知器 (MLP) 来构建压力预测回归模型.
  • 开发了个别传感器的最佳MLP模型,从相关传感器数据中推断压力.

主要成果:

  • 在培训和测试数据集上,MLP回归模型显示出强大的匹配性能.
  • 该方法成功地确定了关键的压力传感器,这些传感器对于应力恢复至关重要.
  • 废弃试验证实了拟议方法的稳定性和有效性.

结论:

  • 机器学习方法为船舶SHM提供了有效的应力分布分析.
  • 识别关键传感器可以提高监控系统的可解释性和效率.
  • 该方法为SHM机制提供了宝贵的见解,并提高了结构安全.