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Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

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

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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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Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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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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Tensile Strength Considerations of Concrete01:16

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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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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.
As the concrete specimen fractures under...
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Impact Strength of Concrete01:21

Impact Strength of Concrete

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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...
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Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior
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Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.

Mohammad Reza Akbarzadeh1, Hossein Ghafourian2, Arsalan Anvari3

  • 1Department of Civil Engineering, Sharif University of Technology, Tehran 1136511155, Iran.

Materials (Basel, Switzerland)
|June 10, 2023
PubMed
Summary

This study introduces an artificial neural network (ANN) optimized by electromagnetic field optimization (EFO) for predicting concrete compressive strength (CCS). The ANN-EFO model demonstrates superior accuracy and speed compared to other optimization methods for reliable CCS estimation.

Keywords:
civil engineeringconcrete compressive strengthelectromagnetic field optimizationmetaheuristic strategies

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Computational Intelligence

Background:

  • Concrete compressive strength (CCS) is a critical mechanical property for concrete structures.
  • Accurate prediction of CCS is essential for quality control and structural integrity.
  • Existing prediction methods may lack efficiency or accuracy.

Purpose of the Study:

  • To develop a novel and efficient method for predicting concrete compressive strength (CCS).
  • To optimize an artificial neural network (ANN) using electromagnetic field optimization (EFO) for CCS prediction.
  • To compare the performance of EFO with other optimization algorithms (WCA, SCA, CFOA).

Main Methods:

  • An artificial neural network (ANN) model was developed for CCS prediction.
  • The ANN was optimized using the electromagnetic field optimization (EFO) algorithm.
  • EFO's performance was benchmarked against the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA).
  • Key concrete parameters (cement, slag, fly ash, water, superplasticizer, aggregates, age) were used as inputs.

Main Results:

  • The ANN optimized with EFO (ANN-EFO) achieved the lowest mean absolute error (5.6236) in CCS prediction.
  • ANN-EFO demonstrated higher prediction accuracy compared to ANN-WCA (5.8363), ANN-CFOA (7.6538), and ANN-SCA (7.8248).
  • The EFO algorithm was significantly faster than the other optimization strategies evaluated.

Conclusions:

  • The hybrid ANN-EFO model is a highly efficient and reliable approach for the early prediction of concrete compressive strength.
  • ANN-EFO offers a user-friendly, explainable, and explicit predictive formula for convenient CCS estimation.
  • The study recommends ANN-EFO for practical applications requiring accurate and rapid CCS assessment.