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

Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

635
The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.
635
Cable: Problem Solving01:29

Cable: Problem Solving

312
When dealing with a cable that is fixed to two supports and subjected to uniform loading, it is crucial to determine the maximum tension in the cable. This process can be broken down into several key steps, as outlined below:
312
Cable Subjected to Concentrated Loads01:28

Cable Subjected to Concentrated Loads

794
Flexible cables are commonly used in various applications for support and load transmission. Consider a cable fixed at two points and subjected to multiple vertically concentrated loads. Determine the shape of the cable and the tension in each portion of the cable, given the horizontal distances between the loads and supports.
794
Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

423
Overhead power transmission lines rely on cables to carry electricity across large distances. To ensure the stability and functionality of these lines, it is crucial to understand the shape and tension experienced by the cables under the influence of their weight.
A generalized loading function is employed to analyze a cable subjected to its own weight. This function considers the force acting along the cable's arc length rather than its projected length, providing a more accurate...
423
Maximum Deflection01:13

Maximum Deflection

437
When analyzing beams under unsymmetrical loads, such as a train moving on a bridge, it is crucial to accurately determine the points of maximum stress and deflection. The process involves identifying the maximum deflection of the beam, which may not always occur at its midpoint due to the uneven distribution of the load.
The maximum deflection occurs at a specific point, known as point O, where the tangent to the deflection curve is horizontal. To find point O, the slope of the tangent at any...
437
Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

241
Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
The insights from the bending moment diagram extend to...
241

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

Bridge Damage Identification Using Time-Varying Filtering-Based Empirical Mode Decomposition and Pre-Trained Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2025
Same author

A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms.

Sensors (Basel, Switzerland)·2024
Same author

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data.

Sensors (Basel, Switzerland)·2024
Same author

Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare.

Sensors (Basel, Switzerland)·2023
Same author

Stochastic Propagation of Fatigue Cracks in Welded Joints of Steel Bridge Decks under Simulated Traffic Loading.

Sensors (Basel, Switzerland)·2023
Same author

A Dynamic Analysis of Smart and Nanomaterials for New Approaches to Structural Control and Health Monitoring.

Materials (Basel, Switzerland)·2023

相关实验视频

Updated: Jun 3, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

8.2K

一种基于时间和频率的数据驱动方法,用于识别结构损坏,并将其应用于电缆支桥梁样本.

Naiwei Lu1, Yiru Liu1, Jian Cui1

  • 1School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
概括

本研究引入了一种使用机器学习和时间频率图像分析来识别结构损伤的新方法. 该ResNet模型有效地检测桥梁的损坏,即使有有限的传感器和杂的数据.

关键词:
这是一座悬挂式桥梁.卷积神经网络是一种卷积神经网络.格拉姆角差距场的不同结构损坏的识别和识别结构健康监测 结构健康监测

更多相关视频

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.1K
Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars
10:24

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

6.6K

相关实验视频

Last Updated: Jun 3, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

8.2K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.1K
Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars
10:24

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

6.6K

科学领域:

  • 结构工程是结构工程.
  • 机器学习应用程序 机器学习应用程序
  • 信号处理 信号处理

背景情况:

  • 传统的结构损坏识别依赖于手动的特征提取,往往导致性能不佳.
  • 深度学习方法,如卷积神经网络 (CNN),提供自动特征提取,以提高结构健康监测 (SHM) 的准确性.

研究的目的:

  • 开发一种先进的数据驱动方法来识别复杂结构中的结构损伤.
  • 通过时间频率分析和深度学习,提高现有方法的有效性.

主要方法:

  • 将结构加速信号转换为2D图像,使用Gram角度差异场 (GADF).
  • 使用卷积神经网络 (CNN),特别是ResNet,从图像数据中提取特征并对损坏进行分类.
  • 在移动车辆负载下,在吊桥模型上进行实验验证.

主要成果:

  • 与其他四个传统网络相比,ResNet模型在损害识别准确性和融合速度方面表现优异.
  • 拟议的方法使用甲板上的有限传感器准确地识别了桥梁上的损伤.
  • 预测准确度从86.63%下降到62.5%,因为信号噪声比 (SNR) 从20dB下降到2.5dB,表明了强度.

结论:

  • 使用CNN (ResNet) 的基于时间频率,数据驱动的方法对于结构损坏的识别是有效的.
  • 该方法显示,即使环境噪音和传感器数据有限,在桥梁上也存在实践应用的巨大潜力.