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

Total Voids in Concrete01:12

Total Voids in Concrete

142
Total voids in concrete encompass gel water volume, capillary pores, and entrapped air. Gel water (retained within the cement hydration products) and physically entrapped or adsorbed water are significant for the hydration process. For complete hydration, it's estimated that the space needed for the products of a cubic centimeter of cement doubles. Capillary pores constitute the unoccupied space within the hydrated cement paste, with their size largely influenced by the water-to-cement...
142
Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

102
This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
102
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

148
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...
148
Slump Test01:20

Slump Test

282
The slump test is a widely used method to measure the workability of concrete. It employs a 12-inch high truncated cone mold that tapers from eight inches at the base to four inches at the top. Before testing, the mold is securely attached to a flat base and dampened.
Concrete is poured into the mold in three layers to conduct the test. Each layer is compacted 25 times with a steel tamping rod, which has a five-eighths-inch diameter and a rounded end, to ensure even distribution and eliminate...
282
Shrinkage in Concrete01:27

Shrinkage in Concrete

132
Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
132
Accelerated Curing of Concrete01:25

Accelerated Curing of Concrete

191
Accelerating concrete curing is achieved by applying heat and additional moisture. This process accelerates the hydration of the cement, resulting in an earlier strength gain in the concrete. Steam curing is a method wherein the concrete products are either transported through a chamber on a conveyor belt or encased in plastic, allowing steam at atmospheric pressure to circulate freely around them. This process begins with a phase of moist curing that typically lasts between 3 to 5 hours, after...
191

You might also read

Related Articles

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

Sort by
Same author

ABIGX: A Unified Framework for eXplainable Fault Detection and Classification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Binding of low rank coal polycyclic aromatic hydrocarbons with ABTS mediated bacterial laccase: insight from molecular simulations.

Journal of biomolecular structure & dynamics·2026
Same author

Data ID Extraction Networks for Unsupervised Class- and Classifier-Free Detection of Adversarial Examples.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis.

Sensors (Basel, Switzerland)·2025
Same author

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models.

Sensors (Basel, Switzerland)·2024
Same author

Deep Probabilistic Principal Component Analysis for Process Monitoring.

IEEE transactions on neural networks and learning systems·2024

Related Experiment Video

Updated: Jul 24, 2025

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

11.2K

Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study.

Chihang Wei1, Zhihuan Song2

  • 1School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

New data-driven methods accurately forecast subsurface inclusions in continuous casting slabs, improving product quality and process efficiency. These advanced techniques offer timely detection, outperforming traditional methods.

Keywords:
data-driven methodsdiscriminant analysisreal-time forecastingstack autoencodersubsurface inclusion defects

More Related Videos

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.1K

Related Experiment Videos

Last Updated: Jul 24, 2025

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

11.2K
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.1K

Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Artificial Intelligence

Background:

  • Subsurface inclusions are common defects in continuous casting slabs, impacting final product quality and potentially causing process failures.
  • Traditional detection methods struggle with online identification of these defects, necessitating advanced approaches.
  • Data-driven methods offer a promising, yet under-explored, alternative for defect detection.

Purpose of the Study:

  • To comparatively study data-driven methods for detecting subsurface inclusions in continuous casting.
  • To develop and evaluate novel models: scatter-regularized kernel discriminative least squares (SR-KDLS) and stacked defect-related autoencoder back propagation neural network (SDAE-BPNN).
  • To demonstrate the feasibility, accuracy, and efficiency of these data-driven methods in real-world applications.

Main Methods:

  • Development of a scatter-regularized kernel discriminative least squares (SR-KDLS) model for direct forecasting.
  • Implementation of a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) for deep feature extraction.
  • Comparative analysis using case studies from a real-life continuous casting process.

Main Results:

  • The developed SR-KDLS and SDAE-BPNN models achieved timely (within 0.01 ms) and accurate forecasting of subsurface inclusions.
  • Demonstrated superior performance compared to common methods, evidenced by significantly higher F1 scores.
  • Validated the efficiency and computational merits of the proposed data-driven approaches.

Conclusions:

  • Data-driven methods, particularly SR-KDLS and SDAE-BPNN, are highly effective for online detection of subsurface inclusions.
  • These advanced models enhance the quality control of continuous casting and mitigate risks associated with defects.
  • The study highlights the potential of AI in revolutionizing defect detection in industrial manufacturing.