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Classifying High Strength Concrete Mix Design Methods Using Decision Trees.

Saleh J Alghamdi¹

¹Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

Materials (Basel, Switzerland)

|March 10, 2022

Summary

Determining concrete mix design methods is crucial for engineers. This study uses a decision tree model to accurately classify methods based on concrete proportions, identifying cement content as the key predictor.

Keywords:

compressive strength high strength concrete machine learning mix design

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Area of Science:

Civil Engineering
Materials Science
Data Science

Background:

Concrete mix design methods are essential for achieving desired workability and strength.
Different methods yield distinct ingredient proportions, making identification challenging for engineers.
Accurate identification of mix design methods is vital for construction quality control.

Purpose of the Study:

To develop a model for classifying concrete mix design methods using only mix proportions.
To assess the accuracy of a decision tree model in distinguishing between different design methodologies.
To identify the most influential concrete components in determining the design method.

Main Methods:

A decision tree model was trained to classify concrete mix design methods.
Dimensionality reduction techniques were employed to identify key predictors.
The model was evaluated based on its classification accuracy.

Main Results:

The decision tree model achieved high accuracy in classifying concrete mix design methods.
Cement content was identified as the most significant predictor for determining the mix design method.
A novel, high-accuracy model for method determination based on proportions was proposed.

Conclusions:

The proposed decision tree model offers a reliable approach to identifying concrete mix design methods from proportions.
Understanding the primary predictors, like cement content, enhances the practical application of this model.
This research provides a valuable tool for construction engineers in quality assurance and mix design verification.