The Application of Machine Learning on Concrete Samples
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Date
2023
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Abstract
Machine learning is a branch of artificial intelligence that helps computers to learn from data and make predictions based on patterns identified in big data. The purpose of this study is to explore the applicability of machine learning models in classifying the compressive strength of concrete specimens with different types of ingredients. Despite the investigations in the literature about estimating concrete density, there is no relevant study on categorizing compressive strength. To address this gap, in this study, three machine learning classification algorithms (Decision Tree, Naive Bayes Classifier, and K-Nearest Neighbors) are employed to classify concrete samples. The performance of each algorithm is evaluated and compared. The results show that the Decision Tree classifier provides the best performance with an average precision and recall of 99%, f1-score of 0.99, and accuracy of 99%. Moreover, the study provides insights into the application of ML algorithms in a real-world dataset. This study demonstrates that machine learning is a powerful tool that can be used to improve the accuracy of concrete strength classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Keywords
Classifiers , Compressive strength , Concretes , Decision trees , Learning algorithms , Nearest neighbor search , Compressive strength of concrete , Concrete , Concrete samples , Decision-tree algorithm , Learn+ , Machine learning models , Machine-learning , Naive-Bayes algorithm , Performance , Prediction-based , Machine learning