Jove
Visualize
Contact Us

Related Concept Videos

Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

651
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.
651
Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

428
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...
428
Cable Subjected to Concentrated Loads01:28

Cable Subjected to Concentrated Loads

799
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.
799
Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

267
One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
267
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

158
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
158
Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

256
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...
256

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Passive Control in a Continuous Beam under a Traveling Heavy Mass: Dynamic Response and Experimental Verification.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

993

Convolution Neural Network Development for Identifying Damage in Vibrating Pylons with Mass Attachments.

George D Manolis1, Georgios I Dadoulis1

  • 1Laboratory for Experimental Strength of Materials and Structures, School of Civil Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

A convolution neural network (CNN) detects pylon damage using vibration data. This AI model interprets structural responses to identify damage onset, improving structural health monitoring.

Keywords:
autoencodersconvolution neural networksdamage detectionfracturemachine learningprincipal component analysispylonsstructural dynamicsvibrations

More Related Videos

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
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

Related Experiment Videos

Last Updated: Jun 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

993
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
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

Area of Science:

  • Structural Engineering
  • Artificial Intelligence
  • Vibration Analysis

Background:

  • Pylon damage detection often relies on sensitive indicators, but their effectiveness is limited by low-amplitude responses to environmental loads.
  • Existing methods struggle with early damage detection due to low sensitivity of damage indicators.

Purpose of the Study:

  • To develop and evaluate a convolution neural network (CNN) for detecting pylon damage based on vibratory response.
  • To interpret experimental data using a mathematical model for enhanced damage detection accuracy.

Main Methods:

  • A mathematical model was created to interpret experimental data from a fixed-base pylon with transverse motion.
  • Damage was simulated in the model using springs representing beam cracking.
  • Numerically generated acceleration records were used to train a CNN for damage identification.

Main Results:

  • The trained CNN was employed to identify damage from experimental acceleration records.
  • Challenges encountered by the CNN in damage presence/absence identification were analyzed.
  • Strategies for improving the CNN's detection performance were proposed.

Conclusions:

  • CNNs show potential for pylon damage detection using vibration analysis.
  • Further research is needed to overcome limitations and enhance CNN accuracy in structural health monitoring.
  • The study highlights the importance of data quality and model refinement for AI-driven damage detection.