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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.
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Pozzolans

Pozzolans

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.
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Design Example: Managing Concrete Workability

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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.

Effects of Air-entrainment in Concrete

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Fatigue Strength of Concrete

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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep

Bhupesh P Nandurkar¹, Jayant M Raut¹, Pawan K Hinge¹

¹Department of Civil Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, 441110, Maharashtra, India.

Scientific Reports

|August 21, 2025

Summary

This study introduces a hybrid AI model for accurate concrete strength prediction, incorporating fly ash. The interpretable model enhances construction safety and material design by providing clear insights into strength factors.

Keywords:

AutoML optimization Concrete compressive strength Deep neural networks Fly ash reactivity Gradient boosting Multitask learning framework Non-destructive testing SHAP and LIME interpretability

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Area of Science:

Materials Science and Engineering
Civil Engineering
Artificial Intelligence in Construction

Background:

Accurate concrete strength prediction is crucial for construction safety and quality assurance.
Existing methods often compromise between accuracy and interpretability, particularly with supplementary cementitious materials like fly ash.
The need for interpretable models that can handle complex mix designs and environmental factors is paramount.

Purpose of the Study:

To develop a highly accurate and interpretable hybrid model for predicting concrete compressive and tensile strength.
To integrate mix design variables, environmental factors, and non-destructive testing (NDT) data within a multitask learning (MTL) framework.
To leverage advanced machine learning techniques for enhanced prediction accuracy and model explainability.

Main Methods:

A hybrid approach combining gradient boosting (XGBoost) and deep neural networks (DNNs) was employed.
AutoGluon was utilized for automated model optimization within a multitask learning (MTL) framework.
Explainability was achieved using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for global and local interpretation.

Main Results:

The model achieved an impressive R² score of 0.91 on the test set.
A 23% reduction in Mean Squared Error (MSE) was observed, surpassing existing models.
Feature analysis indicated that fly ash percentage significantly influences predictions, contributing approximately 25%.

Conclusions:

The proposed hybrid model offers a robust platform for interpretable concrete strength prediction.
The findings demonstrate a significant advancement in bridging hybrid modeling, automated optimization, and explainability for concrete applications.
This work holds substantial promise for optimizing material design and ensuring structural integrity in construction.