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Alkali Aggregate Reaction in Concrete01:26

Alkali Aggregate Reaction in Concrete

90
The alkali-aggregate reaction in concrete involves natural siliceous minerals in aggregates reacting with alkaline hydroxides derived from cement alkalis. This reaction forms an alkali-silica gel that absorbs water, swells, and increases in volume, which is confined by the surrounding cement paste, creating internal pressures that crack and disrupt the concrete. The extent of expansion and damage can be partly attributed to the alkali-silica reaction's osmotic hydraulic pressure and the...
90
Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

302
Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...
302
Pozzolans01:21

Pozzolans

102
Pozzolans are siliceous or aluminous materials blended with Portland cement. They interact with the calcium hydroxide produced during the hydration of Portland cement and contribute to improved strength and durability of concrete. The pozzolanic activity, a measure of a pozzolan's effectiveness, is typically assessed using the strength activity index, as defined in ASTM C 618-93, which calculates the ratio of the compressive strength of cement mixtures with and without pozzolan.
Fly ash is...
102
Qualitative Analysis03:46

Qualitative Analysis

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For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...
22.0K
Design Example: Managing Concrete Workability01:14

Design Example: Managing Concrete Workability

74
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.
74
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

91
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...
91

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Related Experiment Video

Updated: Jun 11, 2025

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine Learning.

Junfei Zhang1, Shenyan Shang1, Zehui Huo1

  • 1School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China.

Materials (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts alkali-activated material (AAM) strength using fly ash (FA) and granulated blast furnace slag (GBFS). Water content and curing periods are key factors for developing high-performance, sustainable construction materials.

Keywords:
alkali-activated materialsfly ashgranulated blast furnace slagmachine learningstrength

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Computational Materials Science

Background:

  • Alkali-activated materials (AAMs) using fly ash (FA) and granulated blast furnace slag (GBFS) are vital for sustainable construction.
  • Understanding their strength development is critical for effective material design and application.

Purpose of the Study:

  • To investigate the strength development mechanism of FA-GBFS-based AAMs using machine learning.
  • To develop a predictive model for AAMs' uniaxial compressive strength (UCS).

Main Methods:

  • Collected 616 UCS data points from published literature on FA-GBFS AAMs.
  • Trained and evaluated four tree-based machine learning models, including Gradient Boosting Regression (GBR).
  • Utilized SHapley Additive exPlanations (SHAP) to identify key influential variables.

Main Results:

  • Gradient Boosting Regression (GBR) achieved the highest prediction accuracy (R-value=0.970, RMSE=4.110 MPa).
  • Water content was identified as the most significant factor influencing strength, followed by curing period.
  • Optimal elemental ratios were recommended: Ca:Si ≈ 1.3, Na:Al < 1, Si:Al > 3.

Conclusions:

  • The developed machine learning model accurately predicts AAM strength.
  • The findings provide practical guidance for designing high-performance, sustainable AAMs.
  • Laboratory validation confirmed the model's reliability and potential for practical application.