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 Experiment Video

Updated: Jul 16, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Identification of Acoustic Emission Spectrograms from Limestone Fracturing Based on a Novel Deep Learning Model.

Yan Zhang1,2, Daojing Guo3, Yulong Ye1,2

  • 1Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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

Mining biomarkers for type 2 diabetic nephropathy based on urinary proteomics and metabolomics.

Frontiers in endocrinology·2026
Same author

Fertility planning for women in medicine: a call for an active approach.

International journal of women's dermatology·2026
Same author

Beyond traditional metrics: the holistic role of multisource feedback in comprehensive dental clinical education.

Frontiers in medicine·2026
Same author

Anlotinib combined with neoadjuvant chemotherapy for HR+/HER2- breast cancer (ACNTBC): a prospective, single-arm, single-center phase II clinical study with real-world validation.

Signal transduction and targeted therapy·2026
Same author

Engineered Optogenetic Circuits In Yeast with Self-Sustained Outputs.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A Generalizable Active-Site Blocking Strategy Enables High Initial Coulombic Efficiency in Mononitrogen-Containing Organic Cathodes.

Angewandte Chemie (International ed. in English)·2026

A new deep learning model, Principal Component Analysis (PCA)-Visual Geometry Group 16 (VGG16), accurately identifies limestone fractures using acoustic spectrograms. This method enhances fracture detection and aids in predicting rock failure.

Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Rock mass integrity is threatened by progressive microscopic fractures, a key factor in macroscopic failure.
  • Accurate identification of these fractures is crucial for rock engineering safety.

Purpose of the Study:

  • To develop a novel deep learning model for identifying limestone fractures.
  • To improve the accuracy and efficiency of fracture detection in rock masses.

Main Methods:

  • A Principal Component Analysis (PCA)-Visual Geometry Group 16 (VGG16) deep learning model was developed.
  • The model integrates PCA for linear feature purification before VGG16 classification.
  • Acoustic emission signals from triaxial compression tests on limestone were analyzed.
Keywords:
PCA-VGG16 modelacid-alkali pretreatmentacoustic emissiondeep learningimage recognition

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Related Experiment Videos

Last Updated: Jul 16, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Main Results:

  • The PCA-VGG16 model achieved 19.19% and 10.77% higher accuracy than conventional CNN and VGG16 models, respectively.
  • Training time was reduced by 35.00% (vs. CNN) and 23.53% (vs. VGG16).
  • The model accurately identified microscopic fracture characteristics in limestone.

Conclusions:

  • The PCA-VGG16 model offers superior performance for identifying internal microscopic fracture characteristics in limestone.
  • Integrating acoustic emission signals with deep learning effectively quantifies fracture levels and predicts rock failure.