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

Structural Classification of Joints01:20

Structural Classification of Joints

3.6K
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...
3.6K
Method of Joints01:30

Method of Joints

854
The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint.
Since plane truss members are in the same plane, each joint is subjected to a coplanar and concurrent force system. To apply the method of joints, the first step is to...
854
Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

632
Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
632
Method of Joints: Problem Solving I01:30

Method of Joints: Problem Solving I

1.2K
The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint. Consider a truss structure with two forces of 20 N and 10 N acting at joints C and D, respectively. The method of joints can be used to determine the forces FCB, FDC,...
1.2K
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

1.3K
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...
1.3K
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

3.7K
Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Does Pain Self-efficacy influence Initial Forward Bending in Adults with Chronic Low Back Pain following Exercise? A Cohort Study.

International journal of sports physical therapy·2025
Same author

Proanthocyanidin B2 regulates epidermal growth factor receptor (EGFR) and activates the PI3K/AKT pathway to promote the in vitro maturation of sheep oocytes.

Theriogenology·2025
Same author

Working Memory Load-Dependent Cortical Mechanism of Distraction Analgesia in Healthy Individuals: An fNIRS Study.

Journal of pain research·2025
Same author

Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.

World journal of gastroenterology·2025
Same author

Analysis of the efficacy and prognostic factors of gemcitabine combined with oxaliplatin in non-Hodgkin lymphoma.

Oncology letters·2025
Same author

Metabolic phenotypes and pulmonary function in aging: the mediating roles of cognitive function and depression in CHARLS.

BMC public health·2025

Related Experiment Video

Updated: Aug 1, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

557

Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an

Wen Wu1, Sergio Cantero-Chinchilla2, Wang-Ji Yan3,4

  • 1Institute for Aerospace Technology, Resilience Engineering Research Group, The University of Nottingham, Nottingham NG7 2RD, UK.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary

This study introduces a Bayesian framework for detecting defects in aluminum joints using guided wave monitoring. The method accurately identifies damage by analyzing scattering coefficients, improving computational efficiency for structural health monitoring.

Keywords:
Bayesian inferencedamage identificationguided waveshybrid wave and finite elementjoints/bounded structuressurrogate model

More Related Videos

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.3K
Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

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

Published on: September 29, 2019

8.3K

Related Experiment Videos

Last Updated: Aug 1, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

557
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

2.3K
Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

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

Published on: September 29, 2019

8.3K

Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Non-Destructive Testing

Background:

  • Guided wave monitoring is crucial for structural health assessment.
  • Accurate defect detection in complex structures like aluminum joints remains challenging.
  • Existing methods often struggle with uncertainties and computational demands.

Purpose of the Study:

  • To develop and validate a robust defect detection and identification scheme for aluminum joints.
  • To enhance the computational efficiency of guided wave-based damage identification.
  • To investigate the influence of sensor placement on identification accuracy.

Main Methods:

  • Guided wave testing was employed, focusing on scattering coefficients as a damage feature.
  • A Bayesian framework was developed to handle modeling and experimental uncertainties.
  • A hybrid wave and finite element (WFE) approach, coupled with a kriging surrogate model, was used for efficient defect size prediction.

Main Results:

  • The proposed Bayesian framework successfully identified defects in aluminum joints.
  • The hybrid WFE and kriging surrogate model significantly improved computational efficiency.
  • Numerical and experimental studies validated the effectiveness of the damage identification scheme.

Conclusions:

  • The developed approach provides a reliable and computationally efficient method for defect detection in aluminum joints.
  • The framework's ability to account for uncertainties enhances its practical applicability.
  • Sensor location is a critical factor influencing the accuracy of damage identification.