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Related Concept Videos

Fiber Reinforced Concrete01:22

Fiber Reinforced Concrete

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Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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Reinforcements in Concrete01:25

Reinforcements in Concrete

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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

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

Non-destructive Tests for Concrete Strength

122
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...
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Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

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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...
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Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

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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.
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Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive

Yassir M Abbas1, Mohammad Iqbal Khan1

  • 1Department of Civil Engineering, College of Engineering, King Saud University, Riyadh 800-11421, Saudi Arabia.

Materials (Basel, Switzerland)
|November 25, 2023
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Summary

Machine learning (ML) accurately forecasts steel-fiber-reinforced concrete (SFRC) properties. An extra gradient boosting model identifies optimal SFRC mixes, enhancing construction engineering practices.

Keywords:
feature importancegraphical user interfacemachine learningpartial dependence plotsprediction modelsteel-fiber-reinforced concrete (SFRC)

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Area of Science:

  • Construction Engineering
  • Materials Science
  • Data Science

Background:

  • Construction engineering increasingly integrates machine learning (ML) for material property forecasting.
  • Existing models for steel-fiber-reinforced concrete (SFRC) often lack transparency and practical applicability.
  • There is a need for advanced, interpretable ML models to predict SFRC characteristics.

Purpose of the Study:

  • To develop and validate an accurate machine learning model for predicting the compressive strength of SFRC.
  • To identify optimal SFRC mix designs using advanced ML techniques.
  • To bridge the gap between theoretical ML models and practical applications in construction.

Main Methods:

  • Utilized the extra gradient (XG) boosting algorithm for predictive modeling.
  • Compiled a comprehensive database from 43 publications (420 records) focusing on crimped, hooked, and mil-cut fibers.
  • Conducted experimental validation with 20 SFRC mixtures and employed partial dependence plots (PDPs) for analysis.

Main Results:

  • The XG boosting model achieved high accuracy, with a mean target-prediction ratio of 99% on independent datasets.
  • Identified optimal SFRC formulations with enhanced compressive strength.
  • Partial dependence plots revealed key relationships between input parameters and SFRC compressive strength.

Conclusions:

  • The developed ML model offers a transparent and accurate tool for predicting SFRC compressive strength.
  • The study provides practical insights for optimizing SFRC mix designs in construction.
  • A user-friendly digital interface was created to facilitate professional adoption and application.