Jove
Visualize
Contact Us

Related Concept Videos

Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

2.5K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
2.5K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

434
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
434

You might also read

Related Articles

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

Sort by
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: Jan 16, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.5K

Underwater Pile Foundation Defect Detection Method Based on Diffusion Probabilistic Model and Improved PointMLP.

Tongyuan Ji1,2, Dingwen Zhang1

  • 1School of Transportation, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting underwater pile foundation damage using a diffusion probability model and improved PointMLP. The technique achieves high accuracy in identifying defects, offering crucial support for infrastructure safety.

Keywords:
PointMLPattention mechanismdiffusion probabilistic modelpile foundation defect detectionpoint cloud

More Related Videos

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
06:20

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging

Published on: April 28, 2022

2.5K
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

5.6K

Related Experiment Videos

Last Updated: Jan 16, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.5K
Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
06:20

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging

Published on: April 28, 2022

2.5K
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

5.6K

Area of Science:

  • Civil Engineering
  • Geotechnical Engineering
  • Computer Vision

Background:

  • Underwater pile foundations are critical infrastructure.
  • Damage detection is essential for structural integrity and safety.
  • Existing methods may lack accuracy or efficiency.

Purpose of the Study:

  • To develop and validate a new method for detecting damage in underwater pile foundations.
  • To improve the accuracy and reliability of defect detection.
  • To provide technical support for underwater infrastructure inspection.

Main Methods:

  • Utilizing sonar systems for point cloud data acquisition.
  • Applying PCA-ICP registration, filtering algorithms, and RANSAC for point cloud processing.
  • Employing a diffusion probability model to generate and enhance defect point clouds.
  • Integrating a feature attention mechanism into PointMLP for defect identification.

Main Results:

  • Successfully collected and processed point cloud data of a wharf pile foundation.
  • The improved PointMLP effectively identified pile foundation defects.
  • Achieved up to 95% accuracy in calculated volume assessment.
  • Demonstrated a volume error of 0.0756 m³ with 95.238% accuracy.

Conclusions:

  • The proposed method offers a robust solution for underwater pile foundation damage detection.
  • High accuracy in volume calculation and defect identification was achieved.
  • This technique provides valuable technical support for preventing major accidents related to foundation failure.