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Mass Concreting

Mass Concreting

Mass concreting refers to the process of placing large volumes of concrete, such as in gravity dams. The heat generated during the cement hydration process and differential cooling rates within the concrete mass can lead to a temperature gradient, which can result in thermal cracks in the concrete mass.
To reduce the risk of such cracking, the concrete mix may incorporate low-heat cement and pozzolans to reduce the temperature rise. Pre-cooled angular aggregates and water-reducing admixtures...

Measurement of Air Content in Concrete

Measurement of Air Content in Concrete

Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
The pressure method,...

Hot Weather Concreting

Hot Weather Concreting

Concreting at elevated temperatures accelerates the hydration process, leading to quicker setting but potentially reducing the long-term strength of the concrete structure. Additionally, low air humidity fosters rapid moisture loss from the concrete, resulting in reduced workability, pronounced plastic shrinkage, and a higher likelihood of crazing.
Mitigating the heat increase in concrete can be economically achieved by shading aggregate stockpiles to prevent heating from solar radiation,...

Cold Weather Concreting

Cold Weather Concreting

When freshly poured concrete is exposed to freezing temperatures before it has set, the water within the concrete can freeze. This expansion disrupts the setting process, delays chemical reactions necessary for hardening, and increases the volume of pores within the hardened concrete, which weakens its overall structure. If the concrete manages to reach an appreciable strength before it freezes, the damage can be somewhat mitigated.
To counteract the negative impacts of cold weather, ensuring...

Accelerated Curing of Concrete

Accelerated Curing of Concrete

Accelerating concrete curing is achieved by applying heat and additional moisture. This process accelerates the hydration of the cement, resulting in an earlier strength gain in the concrete. Steam curing is a method wherein the concrete products are either transported through a chamber on a conveyor belt or encased in plastic, allowing steam at atmospheric pressure to circulate freely around them. This process begins with a phase of moist curing that typically lasts between 3 to 5 hours, after...

Design Example: Managing Concrete Workability

Design Example: Managing Concrete Workability

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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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

Identification of Adiabatic Temperature Rise Characteristics for Mass Concrete Using the Physics-Informed Neural

Jae Min Lee¹, Chang Joon Lee¹, WoonSeong Jeong¹

¹Department of Architectural Engineering, Chungbuk National University, 1 Chungdae-ro, Heungdeok-gu, Cheongju 28644, Republic of Korea.

Materials (Basel, Switzerland)

|October 29, 2025

Summary

Physics-Informed Neural Networks (PINN) accurately identify adiabatic temperature rise (ATR) characteristics in mass concrete. This method proves reliable even with noisy, short-term data, showcasing its robustness for parameter identification.

Keywords:

adiabatic temperature rise characteristics heat of hydration mass concrete parameter identification physics-informed neural network

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Last Updated: Jan 13, 2026

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

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Published on: July 5, 2024

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Published on: January 5, 2024

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

Computational Mechanics
Materials Science
Artificial Intelligence in Engineering

Background:

Accurate prediction of adiabatic temperature rise (ATR) is crucial for mass concrete structures to prevent thermal cracking.
Traditional methods for identifying ATR characteristics can be complex and data-intensive.
Physics-Informed Neural Networks (PINNs) offer a novel approach by integrating physical laws into neural network training.

Purpose of the Study:

To investigate the capability of PINNs for identifying key ATR characteristics in mass concrete.
To determine the influence of data observation period and noise on PINN performance for parameter identification.
To assess the reliability and consistency of the PINN-based approach.

Main Methods:

Utilized virtual experimental data generated from numerical simulations of three different ATR models.
Employed PINNs to identify parameters defining maximum ATR and temperature increasing rate.
Introduced varying levels of noise and observation periods to evaluate robustness and performance.

Main Results:

PINNs successfully identified the unknown ATR parameters with high accuracy, even using short-term observation data.
Parameter identification performance was influenced by the data observation period but remained robust against introduced noise.
Statistical analysis of 10 independent training sessions confirmed the significant reliability and consistency of the PINN method.

Conclusions:

PINNs provide a robust and reliable tool for identifying adiabatic temperature rise characteristics in mass concrete.
The method demonstrates effectiveness even under challenging data conditions, such as limited observation times and data noise.
This study validates the potential of PINNs for inverse problems in civil engineering applications.