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Related Concept Videos

Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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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.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
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Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Toughness and Hardness of Aggregate01:22

Toughness and Hardness of Aggregate

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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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Specific Gravity of Aggregate01:19

Specific Gravity of Aggregate

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Aggregates typically contain pores, which can be either permeable or impermeable. Considering the pores in the aggregates, the specific gravity of aggregates is defined in three different forms, namely, bulk or gross specific gravity, apparent specific gravity, and absolute specific gravity.
Bulk or gross specific gravity is calculated by taking the ratio of the mass of aggregates in the saturated surface-dry state to the total volume that includes both the solids and the voids within the...
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Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

182
This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
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Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

336
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.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
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Application of Design Aspects in Uniaxial Loading Machine Development
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The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by

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  • 1Department of Materials Engineering, Kyonggi University, Suwon 16227, Korea.

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|December 10, 2020
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This study optimized artificial lightweight aggregate manufacturing using machine learning. Support Vector Regression (SVR) accurately predicted aggregate properties, improving process design.

Keywords:
lightweight aggregatemachine learningorthogonal array experiment design methodsintering processsupport vector regression

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Data Science

Background:

  • Artificial lightweight aggregates (ALAs) are crucial in construction for reducing structural weight.
  • Optimizing ALA production processes is essential for cost-effectiveness and performance.
  • Current manufacturing methods often lack precise control over critical parameters.

Purpose of the Study:

  • To experimentally design and optimize the drying, calcination, and sintering of ALAs.
  • To develop a machine learning model for predicting ALA manufacturing outcomes.
  • To enhance the data-driven understanding of ALA production.

Main Methods:

  • Utilized an L18 orthogonal array for experimental design of ALA processing parameters.
  • Expanded experimental data to 486 instances for robust model training.
  • Applied machine learning techniques including linear regression, random forest, and Support Vector Regression (SVR).

Main Results:

  • Support Vector Regression (SVR) demonstrated superior predictive performance for ALA properties.
  • The developed SVR model accurately predicted measured values.
  • The model showed effectiveness in forecasting outcomes for untested process conditions.

Conclusions:

  • Machine learning, particularly SVR, offers a powerful tool for optimizing ALA manufacturing.
  • Experimental design combined with data analytics can significantly improve process efficiency and product quality.
  • This approach provides a scalable method for predicting and controlling ALA production.