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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.

Workability of Concrete

Workability of Concrete

The workability of concrete is a crucial property that affects its handling, placing, and finishing during construction. It describes the ease with which concrete can be mixed, placed, compacted, and finished. Workability is primarily concerned with the concrete's movement and its ability to resist internal friction and external resistance from molds and reinforcements during the application process.
Concrete's workability is determined by its resistance to internal forces that arise...

Abrasion Resistance of Concrete

Abrasion Resistance of Concrete

Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...

Additives and Fillers in Concrete

Additives and Fillers in Concrete

Additives and fillers are integral to enhancing the properties of concrete. Pozzolans and blast-furnace slag are additives or admixtures due to their reactions with calcium hydroxide released during cement hydration. Fillers, which are finely ground and similar in fineness to Portland cement, improve concrete attributes such as workability density, and reduce capillary bleeding or cracking. Some fillers possess hydraulic properties or participate in benign reactions within the cement paste.
The...

Dynamic Modulus of Elasticity of Concrete

Dynamic Modulus of Elasticity of Concrete

The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by a...

Elasticity in Concrete

Elasticity in Concrete

Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...

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Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning.

Yue Gu¹, Ruyan Fan¹, Yikun Li²

¹College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China.

Nanomaterials (Basel, Switzerland)

|September 26, 2025

Summary

This summary is machine-generated.

Machine learning accurately predicts nano-silica modified concrete (NSC) strength. An optimization framework using NSGA-II balances strength, cost, and carbon emissions for sustainable concrete mix design.

Keywords:

NSGA-II algorithm XGBoost algorithm concrete machine learning multi-objective optimization

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Assessing Disaster Resilience of Concrete with Titanium Dioxide Nanoparticles

Assessing Disaster Resilience of Concrete with Titanium Dioxide Nanoparticles

Published on: November 14, 2025

Area of Science:

Materials Science
Civil Engineering
Computational Science

Background:

Nano-silica modified concrete (NSC) is crucial in modern engineering.
Traditional mix proportion design for NSC is inefficient, requiring significant time and resources.

Purpose of the Study:

To develop accurate machine learning models for predicting NSC compressive strength.
To establish a multi-objective optimization framework for sustainable NSC mix design, considering strength, cost, and carbon footprint.

Main Methods:

Four machine learning models (XGBoost, CatBoost, Random Forest, AdaBoost) were trained to predict compressive strength.
The NSGA-II algorithm was utilized for multi-objective optimization based on the best-performing model.
Feature importance analysis was conducted to identify key influencing factors.

Main Results:

XGBoost demonstrated superior predictive accuracy (R² = 0.99, RMSE = 1.80 MPa).
Nano-silica content significantly impacts both strength (0.82) and cost (0.85).
The NSGA-II algorithm generated Pareto-optimal solutions, illustrating trade-offs between compressive strength, cost, and carbon emissions.

Conclusions:

The integrated machine learning and optimization approach enhances the efficiency of NSC mix proportion design.
This methodology offers a valuable reference for developing sustainable and cost-effective concrete formulations.
The study successfully reduces experimental workload while promoting environmentally conscious engineering practices.