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Related Concept Videos

Strength and Heat of Hydration01:29

Strength and Heat of Hydration

The hydration of cement is an exothermic reaction in which heat is generated as cement hydrates. This heat of hydration is critical to cement's strength development. The rate at which this heat is generated affects the temperature rise, with a majority of the heat being released early in the hydration process, half within the first three days, and about 75% within the first week.
The heat of hydration for each cement compound is significant; for instance, tricalcium aluminate (C3A) and...
Hydration of Cement01:24

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Hydration of cement is a chemical reaction between cement particles and water. This process occurs primarily through two mechanisms: through-solution and topochemical. In the through-solution process, anhydrous compounds dissolve into their constituents, hydrates form in the solution, and then precipitate from the supersaturated solution. The topochemical process involves solid-state reactions at the cement particle surface. The through-solution process dominates the topochemical process at the...
Setting Time of Cement01:12

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The setting time of cement refers to the process of cement paste transitioning from a plastic state to a solid state. This process is crucial in construction as it dictates the timeframe for concrete placement, compaction, and finishing. The onset of this solidification is termed the initial set, indicating when the paste becomes unworkable. The final set is when the paste has solidified completely, and further handling or manipulation can no longer affect its shape. The cement strength is...
Testing Water Quality01:14

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
Curing of Concrete01:20

Curing of Concrete

The hydration of cement takes place within the water-filled capillary pores. However, environmental elements can disrupt this process by evaporating water from the concrete surfaces. Sealed concrete with a water-cement ratio below 0.5 experiences self-desiccation, leading to water loss. The water loss in concrete is mitigated by curing. This technique involves keeping the concrete saturated to maintain the necessary temperature and moisture conditions, to optimally fill the spaces in the cement...
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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create ettringite,...

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Updated: May 23, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Machine learning can predict setting behavior and strength evolution of hydrating cement systems.

Tandré Oey1, Scott Jones2, Jeffrey W Bullard2

  • 1Laboratory for the Chemistry of Construction Materials (LC), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, USA.

Journal of the American Ceramic Society. American Ceramic Society
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts cement setting time and mortar strength based on Ordinary Portland Cement (OPC) composition and fineness. This data-driven approach aids in optimizing concrete formulations for cost, CO2 impact, and performance.

Keywords:
cement compositionfinenessmachine learningsettingstrength

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Last Updated: May 23, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
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Published on: January 5, 2024

Detecting the Water-soluble Chloride Distribution of Cement Paste in a High-precision Way
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Detecting the Water-soluble Chloride Distribution of Cement Paste in a High-precision Way

Published on: November 21, 2017

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Civil Engineering

Background:

  • Predicting concrete properties from component attributes and environmental conditions is a long-standing challenge.
  • Machine learning (ML) has shown promise in estimating concrete strength from mixture proportions.
  • Understanding cement binder properties is crucial for concrete performance.

Purpose of the Study:

  • To develop ML estimators for predicting cement paste setting time and mortar strength development.
  • To utilize a diverse dataset of ASTM C150 cements with measured chemical composition and attributes.
  • To establish a data-driven basis for predicting cementitious system properties.

Main Methods:

  • Trained ML estimators using a dataset of ASTM C150 cements.
  • Input features included cement chemical composition and fineness.
  • Output predictions focused on paste setting time and mortar strength development.

Main Results:

  • ML estimators achieved prediction errors comparable to or lower than ASTM test method repeatability.
  • Demonstrated the capability of ML to estimate the influence of OPC composition and fineness on engineering properties.
  • Validated the use of ML for predicting key performance indicators of cementitious systems.

Conclusions:

  • ML provides a reliable method for estimating cement setting time and mortar strength.
  • This approach enables optimization of concrete formulations considering cost, CO2 impact, and performance.
  • Facilitates data-intensive methods for advancing cementitious materials science and engineering.