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Toughness and hardness are critical properties of aggregate materials used in concrete, particularly on pavement surfaces and industrial flooring subjected to heavy loads. Toughness is defined as the aggregate's resistance to failure by impact and is measured by the aggregate impact value (AIV). For this, the aggregate impact value test is performed, wherein the impact is delivered by a standard hammer, which falls freely under its own weight onto the aggregates. The aggregates fragment in...
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Shape and Texture of Coarse Aggregate01:25

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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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.
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In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
Adequate...
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The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
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Predicting the Engineering Properties of Rocks from Textural Characteristics Using Some Soft Computing Approaches.

Davood Fereidooni1, Luís Sousa2

  • 1School of Earth Sciences, Damghan University, Damghan 36716-41167, Iran.

Materials (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

Petrographic and textural characteristics, quantified by the texture coefficient (TC), can predict rock engineering properties. Artificial neural network (ANN) models offer more accurate predictions than multiple regression analysis (MRA).

Keywords:
artificial neural networkengineering propertiesrockstatistical methodtexture coefficient

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Area of Science:

  • Geotechnical Engineering
  • Materials Science
  • Rock Mechanics

Background:

  • Rock properties are crucial for engineering projects.
  • Petrographic and textural characteristics influence rock engineering behavior.

Purpose of the Study:

  • To investigate the relationship between rock texture and engineering properties.
  • To develop predictive models for rock engineering characteristics using texture coefficient (TC).

Main Methods:

  • Laboratory testing of 15 rock samples for engineering properties (e.g., UCS, SRH, UPV).
  • Petrographic and X-ray diffraction analysis to determine texture coefficient (TC).
  • Simple regression analysis (SRA), multiple regression analysis (MRA), and artificial neural network (ANN) for model development.

Main Results:

  • TC showed direct correlations with density, slake durability index (SDI), Schmidt rebound hardness (SRH), ultrasonic P-wave velocity (UPV), and uniaxial compressive strength (UCS).
  • TC exhibited inverse relationships with porosity and water absorption.
  • ANN models demonstrated higher accuracy and lower residual errors in predicting rock properties compared to MRA models.

Conclusions:

  • TC is a reliable parameter for predicting rock engineering properties.
  • ANN models provide superior predictive capabilities for rock engineering characteristics over MRA.
  • SRA, MRA, and ANN are effective methods for predicting rock engineering properties from TC.