Jove
Visualize
Contact Us

Related Concept Videos

Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

181
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...
181
Microcracking in Concrete01:20

Microcracking in Concrete

200
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
200
Reinforced Brick Masonry01:15

Reinforced Brick Masonry

1.2K
Reinforced brick masonry is an advanced construction technique that enhances the structural integrity of brick walls by incorporating steel reinforcements. These reinforcements are either placed within the hollow cores of bricks or sandwiched between two layers of masonry, known as wythes, and are then secured in place with grout. Grout is a fluid mixture composed of Portland cement, aggregate, and water, providing the necessary bonding agent for the steel and brick.
To fortify brick walls...
1.2K
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

266
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
266
Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

189
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...
189
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

508
The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by...
508

You might also read

Related Articles

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

Sort by
Same author

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features.

Life (Basel, Switzerland)·2023
Same author

Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete.

Materials (Basel, Switzerland)·2023
Same author

Effect of Volume Fraction on Shear Mode Properties of Fe-Co and Fe-Ni Filled Magneto-Rheological Elastomers.

Polymers·2022
Same author

An experimental study of tuned liquid column damper controlled multi-degree of freedom structure subject to harmonic and seismic excitations.

PloS one·2022
Same author

In-Plane Seismic Strengthening of Brick Masonry Using Steel and Plastic Meshes.

Materials (Basel, Switzerland)·2022
Same author

Microstructure and Corrosion Behavior of Atmospheric Plasma Sprayed NiCoCrAlFe High Entropy Alloy Coating.

Materials (Basel, Switzerland)·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: Aug 29, 2025

Fragility Assessment of Bovine Cortical Bone Using Scratch Tests
08:36

Fragility Assessment of Bovine Cortical Bone Using Scratch Tests

Published on: November 30, 2017

9.6K

Machine Learning-Based Fragility Assessment of Reinforced Concrete Buildings.

Abdur Rasheed1, Muhammad Usman2, Muhammad Zain2

  • 1Department of Civil Engineering, MY University, Islamabad, Pakistan.

Computational Intelligence and Neuroscience
|September 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Artificial Neural Networks (ANN) and Gene Expression Programming (GEP) for seismic fragility analysis of school buildings in Pakistan. ANN demonstrated superior accuracy in predicting seismic performance compared to GEP.

More Related Videos

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars
10:24

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

6.8K
Determination of the Mechanical Properties of Flexible Connectors for Use in Insulated Concrete Wall Panels
05:26

Determination of the Mechanical Properties of Flexible Connectors for Use in Insulated Concrete Wall Panels

Published on: October 19, 2022

1.7K

Related Experiment Videos

Last Updated: Aug 29, 2025

Fragility Assessment of Bovine Cortical Bone Using Scratch Tests
08:36

Fragility Assessment of Bovine Cortical Bone Using Scratch Tests

Published on: November 30, 2017

9.6K
Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars
10:24

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

6.8K
Determination of the Mechanical Properties of Flexible Connectors for Use in Insulated Concrete Wall Panels
05:26

Determination of the Mechanical Properties of Flexible Connectors for Use in Insulated Concrete Wall Panels

Published on: October 19, 2022

1.7K

Area of Science:

  • Civil Engineering
  • Earthquake Engineering
  • Computational Intelligence

Background:

  • Pakistan faces significant seismic risk, with past earthquakes causing widespread infrastructure collapse and fatalities.
  • Accurate seismic assessment of infrastructure, particularly school buildings in high-risk zones like Muzaffarabad, is crucial for public safety.
  • Traditional fragility analysis using Incremental Dynamic Analysis (IDA) is computationally intensive and costly.

Purpose of the Study:

  • To develop and compare Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models for seismic fragility analysis of school buildings.
  • To establish reliable fragility curves for predicting seismic performance in Muzaffarabad district, Pakistan (Seismic Zone-4).
  • To offer computationally efficient alternatives to traditional numerical methods for seismic assessment.

Main Methods:

  • Fragility curves were initially conceptualized using Incremental Dynamic Analysis (IDA).
  • Soft computing techniques, specifically an optimized Artificial Neural Network (ANN) [5-25-1] feedforward backpropagation model, were employed.
  • Gene Expression Programming (GEP) was also utilized to develop predictive models for fragility curves.
  • A dataset was split for training (70%), validation (15%), and testing (15%) to ensure model reliability.

Main Results:

  • The Artificial Neural Network (ANN) model achieved a high coefficient of determination (R²) of 0.938 in predicting global drift values.
  • The Gene Expression Programming (GEP) model showed a respectable R² of 0.87 for predicting fragility curves.
  • Both ANN and GEP provided viable alternatives to computationally expensive IDA for seismic performance prediction.

Conclusions:

  • Artificial Neural Networks (ANN) offer a more accurate and efficient method for seismic fragility analysis compared to Gene Expression Programming (GEP).
  • The developed ANN model provides a reliable tool for assessing the seismic performance of school buildings in seismically active regions.
  • These soft computing approaches can significantly reduce the time and cost associated with seismic infrastructure assessment.