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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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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
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Updated: May 10, 2025

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Developing Computer Vision Models for Classifying Grain Shapes of Crushed Stone.

Alexey N Beskopylny1, Evgenii M Shcherban'2, Sergey A Stel'makh3

  • 1Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

This study introduces a novel neural network method for analyzing crushed stone grain shape, improving building material quality assessment. This machine learning approach enhances construction processes by accurately characterizing aggregate morphology.

Keywords:
computer visioncrushed stoneflakiness indexneedle-like grainsneural networkplate-like grainspoint cloud processing

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Materials Science

Background:

  • Traditional methods for assessing building materials like crushed stone are often labor-intensive and may lack precision.
  • Intelligent algorithms, specifically machine learning and neural networks, offer advanced capabilities for enhancing construction processes.
  • Characterizing crushed stone grain morphology, particularly flakiness, is crucial for determining its suitability in construction applications.

Purpose of the Study:

  • To develop and evaluate a novel method for characterizing crushed stone grain morphology using 3D computer vision neural networks.
  • To classify crushed stone grains into shape categories (needle-shaped, plate-shaped, cubic) based on their 3D point cloud data.
  • To assess the effectiveness of machine learning models in improving the accuracy and efficiency of building material quality control.

Main Methods:

  • Utilized specially designed 3D computer vision neural networks, including architectures based on PointNet and PointCloudTransformer.
  • Processed 3D images of crushed stone grains represented as point data clouds.
  • Trained classification algorithms to categorize grain shapes into needle-shaped, plate-shaped, and cubic classes.

Main Results:

  • Achieved an accuracy quality metric of 0.86 during the training of both PointNet and PointCloudTransformer models.
  • Demonstrated the capability of neural networks to accurately classify crushed stone grain morphology.
  • The developed method shows potential for reducing manual labor in building material analysis.

Conclusions:

  • Novel 3D computer vision neural networks effectively characterize crushed stone grain morphology.
  • Machine learning-based approaches offer a promising avenue for enhancing the quality control of construction materials.
  • This technology can supplement traditional methods, reducing manual labor and verifying material quality throughout construction stages.