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Related Concept Videos

Strength of Cement01:20

Strength of Cement

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 ASTM C...
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

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 concrete.
Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

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...
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
The dimensions and shape of a concrete specimen also...
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

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...
Fineness of Cement01:15

Fineness of Cement

The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...

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Related Experiment Video

Updated: Jun 27, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Prediction of the Compressive Strength of Tailings-Based Cement Material Using Machine Learning Models with

Zhanming Zhong1, Senrui Deng1, Tao Liu2

  • 1School of Civil and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China.

Materials (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study enhances solid waste utilization by predicting cement compressive strength using mine tailings. The PSO-XGBoost machine learning model accurately forecasts strength, aiding sustainable construction material development.

Keywords:
SHAP analysiscompressive strengthexperimental verificationmachine learningtailings-based cement materials

Related Experiment Videos

Last Updated: Jun 27, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Materials Science
  • Civil Engineering
  • Environmental Science

Background:

  • Mine tailings present a sustainable alternative to traditional cement, promoting solid waste resource utilization.
  • Compressive strength is a key performance indicator for tailings-based cementitious materials.
  • Machine learning (ML) offers efficient and accurate prediction of material properties, but variations in tailings composition introduce uncertainty.

Purpose of the Study:

  • To develop an integrated approach for predicting the compressive strength of tailings-based cementitious materials.
  • To deploy and evaluate optimized ML models incorporating chemical composition and activation methods.
  • To identify key factors influencing compressive strength and validate predictive accuracy.

Main Methods:

  • Four optimized machine learning models were developed to predict compressive strength.
  • Input parameters included tailings' chemical composition and activation methods.
  • Model performance was evaluated using multiple metrics, with SHAP analysis used for feature importance.

Main Results:

  • The PSO-XGBoost model demonstrated superior predictive performance among the evaluated architectures.
  • SHAP analysis identified mechanical grinding, NaOH concentration, gypsum, and tailings proportions as critical features.
  • Experimental validation confirmed the model's high predictive accuracy with an 8.7% error rate.

Conclusions:

  • The study establishes a robust framework for predicting tailings-based cementitious material strength.
  • The findings provide a theoretical foundation for the effective upcycling of solid waste into construction materials.
  • This approach supports sustainable practices in the cement and construction industries.