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

Temperature Dependent Deformation01:12

Temperature Dependent Deformation

138
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
138
Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.1K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.1K
Bending of Members Made of Several Materials01:08

Bending of Members Made of Several Materials

139
In analyzing a structural member composed of two different materials with identical cross-sectional areas, it is crucial to understand how their distinct elastic properties affect the member's response under load. The analysis involves assessing stress and strain distributions using the transformed section concept, which accounts for variations in material properties.
Hooke's Law determines stress in each material, stating that stress is proportional to strain but varies due to each...
139
Stress-Strain Diagram - Ductile Materials01:24

Stress-Strain Diagram - Ductile Materials

615
The stress-strain relationship in ductile materials such as structural steel or aluminium is intricate and progresses through several stages. When a specimen is loaded, it initially exhibits a linear length increase, depicted by a steep straight line on the stress-strain diagram. It indicates the material is elastically deforming and will return to its original shape once unloaded. However, when a critical stress value is reached, plastic deformation begins. This stage sees substantial...
615
Thermal Strain01:19

Thermal Strain

616
Thermal strain is a concept that arises when we consider how temperature changes affect structures. Unlike the conventional assumption that structures remain constant under load, real-world scenarios often involve temperature fluctuations that can significantly impact these structures. Consider a homogeneous rod with a uniform cross-section resting freely on a flat horizontal surface. If the rod's temperature increases, the rod elongates. This elongation is proportional to the temperature...
616
Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

146
In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
The Maximum Shearing Stress Criterion, also known as...
146

You might also read

Related Articles

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

Sort by
Same author

Methodological Basis for Reliable Evaluation of Air Void Structure Parameters Using the 2D Method.

Materials (Basel, Switzerland)·2025
Same author

Estimation of the Spacing Factor Based on Air Pore Distribution Parameters in Air-Entrained Concrete.

Materials (Basel, Switzerland)·2025
Same author

Simplified Method of Estimating the A<sub>300</sub> Micropore Content in Air-Entrained Concrete.

Materials (Basel, Switzerland)·2023
Same author

Development of the Measuring Techniques for Estimating the Air Void System Parameters in Concrete Using 2D Analysis Method.

Materials (Basel, Switzerland)·2020

Related Experiment Video

Updated: Jun 3, 2025

Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric
07:48

Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric

Published on: January 29, 2020

6.5K

A New Approach for Predicting Strength Based on Temperature-Time History Using Two-Parameter Maturity ANN Models.

Jerzy Wawrzeńczyk1

  • 1Faculty of Civil Engineering and Architecture, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland.

Materials (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network model to predict concrete strength development, overcoming limitations of the traditional equivalent maturity time method. The artificial neural network (ANN) accurately estimates compressive strength by considering hydration heat, activation energy, temperature, and time.

Keywords:
artificial neural networksconcretematuritymodellingmortarstrength development

More Related Videos

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
11:11

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

11.0K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

694

Related Experiment Videos

Last Updated: Jun 3, 2025

Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric
07:48

Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric

Published on: January 29, 2020

6.5K
Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
11:11

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

11.0K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

694

Area of Science:

  • Civil Engineering
  • Materials Science
  • Computational Modeling

Background:

  • The equivalent maturity time (te) method is widely used for predicting concrete strength development under varying temperatures.
  • This method relies on activation energy (Ea), a parameter whose variability with cement type, water/cement ratio, temperature, and additives complicates accurate strength prediction.
  • Determining an appropriate Ea value remains a persistent challenge in concrete science.

Purpose of the Study:

  • To develop a new strength-temperature history model using artificial neural network (ANN) analysis.
  • To investigate the influence of hydration heat (Q), activation energy (Ea), temperature (T), and time (t) on concrete strength prediction.
  • To assess the accuracy of an ANN model in estimating relative compressive strength development.

Main Methods:

  • Development of an ANN model with four input parameters: hydration heat (Q), activation energy (Ea), temperature (T), and time (t).
  • Experimental investigation using mortars made with six different cements.
  • Curing specimens at temperatures ranging from 5 to 35 °C and monitoring strength development over 90 days.

Main Results:

  • The ANN analysis method demonstrated sufficient accuracy in estimating relative compressive strength.
  • Hydration heat (Q) was found to significantly influence early-age strength gain.
  • Activation energy (Ea) was identified as a key factor affecting later-stage strength development.

Conclusions:

  • The proposed ANN model offers a robust approach for predicting concrete strength based on temperature variations during curing.
  • The model effectively captures the complex relationships between curing conditions and strength development.
  • This research provides a valuable tool for optimizing concrete construction practices by accurately forecasting strength gain.