Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
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 Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.

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

Related Experiment Video

Updated: Oct 3, 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

1.3K

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification.

Pooya Mohebian1, Seyed Bahram Beheshti Aval1, Mohammad Noori2

  • 1Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

A new Visible Particle Series Search (VPSS) algorithm accurately identifies structural damage. This optimization method uses visibility graphs to efficiently locate and quantify damage in various structures.

Keywords:
health monitoringmeta-heuristic algorithmoptimization methodstructural damage identificationvisible particle series search

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

6.2K
Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
10:10

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

Published on: December 4, 2020

1.9K

Related Experiment Videos

Last Updated: Oct 3, 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

1.3K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
10:10

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

Published on: December 4, 2020

1.9K

Area of Science:

  • Structural Engineering
  • Mechanical Engineering
  • Aerospace Engineering
  • Computational Intelligence

Background:

  • Structural damage identification is critical for safety and functionality across multiple engineering disciplines.
  • Existing methods may face challenges in accuracy, reliability, or computational efficiency for complex structures.

Purpose of the Study:

  • To propose a novel meta-heuristic optimization algorithm for structural damage identification.
  • To enhance the accuracy and efficiency of damage detection in civil, mechanical, and aerospace structures.

Main Methods:

  • Formulation of structural damage identification as an optimization problem.
  • Development and application of the Visible Particle Series Search (VPSS) algorithm.
  • Utilizing the visibility graph technique to map candidate solutions (particle series) into a network for information extraction.

Main Results:

  • The VPSS algorithm demonstrated high accuracy and reliability in identifying structural damage.
  • Numerical simulations confirmed the computational efficiency of the VPSS algorithm.
  • The algorithm successfully identified both the location and extent of damage in tested structures.

Conclusions:

  • The Visible Particle Series Search (VPSS) algorithm is a powerful and efficient tool for structural damage identification.
  • The proposed method offers a reliable approach for ensuring structural integrity and safety.
  • VPSS shows significant potential for practical applications in structural health monitoring.