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

Measurement of Air Content in Concrete01:23

Measurement of Air Content in Concrete

Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it to...
Pumped Concrete01:13

Pumped Concrete

Concrete in large quantities can be pumped across long distances for placing in inaccessible sites. This system comprises a hopper that receives concrete from a mixer, a pump to propel the concrete, and pipelines that facilitate its delivery.
For direct-acting pumps, the concrete enters the pump via the inlet valve under the action of gravity and suction created by the movement of the piston. This concrete is then forced into the pipeline and out through the outlet valve by the forward movement...
Reinforcements in Concrete01:25

Reinforcements in Concrete

Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Ready Mixed Concrete01:26

Ready Mixed Concrete

Ready-mixed concrete, also known as pre-mixed concrete, is prepared in a centralized plant and then transported in trucks to construction sites where it is ready for placement. This type of concrete is categorized into central-mixed, truck-mixed (or transit-mixed), and shrink-mixed. Central-mixed concrete is entirely prepared at a plant and moved to the site in agitator trucks that rotate at a speed of 2 to 6 rpm. Truck-mixed concrete, on the other hand, has the ingredients batched at the plant...

You might also read

Related Articles

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

Sort by
Same author

Optimum Carbon Fiber Reinforced Polymer (CFRP) Design for Flexural Strengthening of Cantilever Concrete Walls Using Artificial Neural Networks.

Polymers·2025
Same author

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.

Biomimetics (Basel, Switzerland)·2024
Same author

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach.

Materials (Basel, Switzerland)·2023
Same author

Optimization and Predictive Modeling of Reinforced Concrete Circular Columns.

Materials (Basel, Switzerland)·2022
Same author

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms.

Materials (Basel, Switzerland)·2022
Same author

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns.

Materials (Basel, Switzerland)·2022
Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026
Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026
Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026
Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026
Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026
Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

Materials (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 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

Image-Based Classification of Concrete Carbonation Using YOLO Models.

Yaren Aydın1, Ümit Işıkdağ2, Sinan Melih Nigdeli1

  • 1Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Türkiye.

Materials (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Detecting concrete carbonation is vital for structural safety. This study introduces a deep learning framework using YOLO models, with YOLOv11m proving most effective for rapid, accurate visual classification of carbonation presence.

Keywords:
YOLOcarbonationclassificationdeep learning

Related Experiment Videos

Last Updated: Jun 13, 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

Area of Science:

  • Civil Engineering
  • Materials Science
  • Computer Science

Background:

  • Concrete carbonation is a critical factor influencing structural integrity and durability.
  • Early detection of carbonation is essential for assessing reinforcement corrosion risk and prioritizing maintenance.
  • Existing research primarily focuses on depth estimation, leaving visual classification of carbonation presence underexplored.

Purpose of the Study:

  • To develop a field-applicable deep learning framework for automated concrete carbonation detection using images.
  • To systematically compare the performance of various YOLO architectures for carbonation classification.
  • To evaluate the suitability of the ConcreteCARB dataset for carbonation classification tasks.

Main Methods:

  • Utilized deep learning, specifically comparing YOLOv8m, YOLOv11m, YOLOv12m, and YOLOv26m architectures.
  • Trained and evaluated models on the ConcreteCARB dataset for visual carbonation presence classification.
  • Assessed classification accuracy, precision, recall, specificity, AUC-ROC, and inference efficiency (latency, FPS).

Main Results:

  • YOLOv8m and YOLOv11m achieved near-perfect classification accuracy (0.9981).
  • YOLOv11m demonstrated superior inference efficiency, exhibiting the lowest latency and highest frames per second (FPS).
  • YOLOv8m and YOLOv26m provided a balance between speed and performance, while YOLOv12m showed slower processing.

Conclusions:

  • YOLOv11m is identified as the most suitable model for real-time concrete carbonation detection applications.
  • The developed deep learning framework offers a robust solution for rapid visual assessment of concrete carbonation.
  • This approach addresses a significant gap in automated structural health monitoring.