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

Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

182
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...
182
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

115
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...
115
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

191
Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
191
Impact Strength of Concrete01:21

Impact Strength of Concrete

189
Impact strength in concrete is a critical measure that reflects the material's capability to endure the forces applied during pile driving and when supporting machinery foundations that experience impulsive loads. It is also essential when handling precast concrete components to prevent accidental damage. The impact strength is assessed by observing the concrete's resistance to repeated impacts and energy absorption capacity. A key indicator of significant damage to concrete is when it...
189
Strength of Cement01:20

Strength of Cement

129
Strength tests for cement are not performed directly on neat cement paste due to difficulty in obtaining consistent, reliable specimens. Instead, cement is typically tested in the form of cement-sand mortar.
For compressive strength tests, ASTM C 109-05 standards prescribe a cement-sand mix ratio of 1:2.75 and a water/cement ratio of 0.485 for making 2-inch cubes. These cubes are mixed, cast, and cured in saturated lime water at 23°C until testing. Flexural strength testing, outlined in...
129
Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

153
Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
153

You might also read

Related Articles

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

Sort by
Same author

Phase engineering of atomically thin magnetic chromium tellurides via molecular beam epitaxy.

Nature communications·2026
Same author

The interaction network of health-related quality of life in elderly people living with HIV: a cross-sectional study in Chongqing, China.

Scientific reports·2026
Same author

A novel strategy for soft tissue sarcoma lattice radiotherapy: integrating X-ray andγ-ray technologies to optimize dose delivery.

Radiation oncology (London, England)·2026
Same author

LYPD1 promotes the progression of lung adenocarcinoma through activating the PI3K/AKT signaling pathway.

Archives of biochemistry and biophysics·2026
Same author

Astrocyte-derived exosome-mediated siRNA delivery combined with quercetin-Mn complex promotes neural repair in spinal cord injury.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Estimating the Impact of WIC on Diet Quality at Age 60 Months among Children Who Participated in the WIC Infant and Toddler Feeding Practices Study-2.

American journal of preventive medicine·2026

Related Experiment Video

Updated: Jun 20, 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

1.0K

Prediction of HPC compressive strength based on machine learning.

Libing Jin1, Jie Duan2,3, Yichen Jin4

  • 1School of Civil Engineering, Henan University of Technology, Zhengzhou, 450000, China. jinlb@haut.edu.cn.

Scientific Reports
|July 22, 2024
PubMed
Summary

Predicting High-Performance Concrete strength is complex. A combined Genetic Algorithm-Support Vector Machine (GA-SVM) model accurately forecasts compressive strength, with the water-binder ratio being the most influential factor.

Keywords:
Compressive strengthGenetic algorithmHigh-performance concreteMachine learning modelParameter analysis

More Related Videos

Production and Analysis of Sporosarcina pasteurii Biocement Bricks Using Custom 3D-Printed Molds for Unconfined Compression Tests
05:38

Production and Analysis of Sporosarcina pasteurii Biocement Bricks Using Custom 3D-Printed Molds for Unconfined Compression Tests

Published on: March 7, 2025

232
Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior
11:07

Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior

Published on: June 27, 2018

11.1K

Related Experiment Videos

Last Updated: Jun 20, 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

1.0K
Production and Analysis of Sporosarcina pasteurii Biocement Bricks Using Custom 3D-Printed Molds for Unconfined Compression Tests
05:38

Production and Analysis of Sporosarcina pasteurii Biocement Bricks Using Custom 3D-Printed Molds for Unconfined Compression Tests

Published on: March 7, 2025

232
Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior
11:07

Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior

Published on: June 27, 2018

11.1K

Area of Science:

  • Materials Science
  • Civil Engineering
  • Computational Mechanics

Background:

  • Accurate prediction of High-Performance Concrete (HPC) compressive strength is crucial for structural integrity.
  • The relationship between HPC components and compressive strength is complex, high-dimensional, and nonlinear.
  • Existing models often struggle with the intricate nature of HPC strength prediction.

Purpose of the Study:

  • To develop an efficient and robust computational strategy for predicting HPC compressive strength.
  • To compare the performance of various machine learning models, including BPNN, SVM, GA-BPNN, and GA-SVM.
  • To enhance the interpretability of predictive models and identify key influencing factors.

Main Methods:

  • Construction of a compressive strength database with 454 data sets using 8 extracted features.
  • Implementation and comparison of four machine learning models: BPNN, SVM, GA-BPNN, and GA-SVM.
  • Application of Grey Relational Analysis (GRA) and Shapley analysis for model interpretability.

Main Results:

  • Combined models (GA-BPNN, GA-SVM) outperformed single models (BPNN, SVM).
  • The GA-SVM model demonstrated superior generalization ability, convergence speed, and prediction accuracy compared to GA-BPNN.
  • The water-binder ratio was identified as the most significant factor influencing HPC compressive strength.

Conclusions:

  • The GA-SVM model offers a robust and accurate approach for HPC compressive strength prediction.
  • The water-binder ratio is a critical parameter that requires careful consideration in HPC mix design.
  • This research provides valuable insights for optimizing HPC formulations and improving predictive accuracy.