Jove
Visualize
Contact Us

Related Concept Videos

Mixing Concrete01:30

Mixing Concrete

270
Concrete mixing ensures a homogenous blend where aggregates are well-coated with cement paste. Concrete mixing is typically done using two main types of mixers: batch and continuous. Batch mixers handle one batch at a time, thoroughly combining materials before discharging and receiving the next batch. In contrast, continuous mixers receive a steady flow of ingredients, mixing them consistently and discharging without interruption. Within batch mixers, tilting drum mixers mix with internal...
270
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

242
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
242
Aggregate Cement Ratio01:21

Aggregate Cement Ratio

449
The Aggregate Cement ratio refers to the weight of aggregate divided by the weight of cement in a concrete mix. Altering this ratio has profound effects on the concrete's properties. This ratio plays a pivotal role in determining the strength, workability, and durability of concrete. When the Aggregate Cement ratio is higher, the mix is leaner, meaning it has less cement paste to lubricate the aggregate, potentially making the concrete less workable. Such mixes, known as lean, enhance the...
449
Mixing Time01:19

Mixing Time

336
The concept of mixing time is significant in producing a uniform concrete mix with the required strength. The mixing period starts once all components are in the mixer. Initially, the mixer is charged with 10% of the water, followed by the consistent addition of solids and then 80% of the water. The remaining water is added later, within the first quarter of the mixing period. The minimum mixing time varies according to the mixer's capacity; for example, mixers with up to 1 cubic yard...
336
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

375
Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
375
Additives and Fillers in Concrete01:29

Additives and Fillers in Concrete

232
Additives and fillers are integral to enhancing the properties of concrete. Pozzolans and blast-furnace slag are additives or admixtures due to their reactions with calcium hydroxide released during cement hydration. Fillers, which are finely ground and similar in fineness to Portland cement, improve concrete attributes such as workability density, and reduce capillary bleeding or cracking. Some fillers possess hydraulic properties or participate in benign reactions within the cement paste.
The...
232

You might also read

Related Articles

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

Sort by
Same author

Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence.

F1000Research·2025
Same author

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.

Materials (Basel, Switzerland)·2023
Same author

Optimization of Fly Ash-Slag One-Part Geopolymers with Improved Properties.

Materials (Basel, Switzerland)·2023
Same author

Investigation of Alkali-Activated Slag-Based Composite Incorporating Dehydrated Cement Powder and Red Mud.

Materials (Basel, Switzerland)·2023
Same author

Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm.

Materials (Basel, Switzerland)·2022
Same author

Effect of Polymers on Behavior of Ultra-High-Strength Concrete.

Polymers·2022
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: Dec 7, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K

Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model.

Itzel Nunez1, Afshin Marani1, Moncef L Nehdi1

  • 1Department of Civil and Environmental Engineering, Western University, London, ON N6G 1G8, Canada.

Materials (Basel, Switzerland)
|October 2, 2020
PubMed
Summary

This study developed advanced machine learning models to accurately predict recycled aggregate concrete (RAC) compressive strength and optimize its mixture design. The gradient boosting regression and particle swarm optimization models offer cost-saving, sustainable concrete solutions with reduced environmental impact.

Keywords:
Gaussian processdeep learninggated recurrent unitgradient boostingmachine learningmodelrecycled aggregate concreteregression trees

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Dec 7, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Area of Science:

  • Civil Engineering
  • Materials Science
  • Environmental Science

Background:

  • Recycled aggregate concrete (RAC) offers a sustainable alternative to traditional concrete, reducing natural aggregate depletion and construction waste.
  • Challenges in RAC include variability of recycled aggregates and inaccurate compressive strength prediction, hindering mixture optimization.
  • Advanced computational techniques are needed for reliable RAC performance assessment and design.

Purpose of the Study:

  • To develop and evaluate state-of-the-art machine learning models for predicting RAC compressive strength.
  • To optimize RAC mixture design for various strength classes using a hybrid optimization approach.
  • To achieve cost-effective and environmentally friendly RAC mixtures.

Main Methods:

  • Development of machine learning models: Gaussian processes, deep learning, and gradient boosting regression.
  • Evaluation of predictive performance for compressive strength.
  • Implementation of a hybrid model combining particle swarm optimization with gradient boosting regression trees for mixture design.

Main Results:

  • Machine learning models demonstrated robust predictive performance for RAC compressive strength.
  • Gradient boosting regression trees achieved the highest prediction accuracy among the evaluated models.
  • The hybrid optimization model successfully generated cost-saving and environmentally conscious RAC mixture designs for different strength classes.

Conclusions:

  • Machine learning provides a powerful tool for accurate prediction of RAC compressive strength.
  • The hybrid particle swarm optimization and gradient boosting regression model is effective for optimizing sustainable RAC mixtures.
  • This approach facilitates the development of eco-friendly concrete with reduced costs and environmental footprint.