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- W4386014987 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." @default.
- W4386014987 created "2023-08-21" @default.
- W4386014987 creator A5079406015 @default.
- W4386014987 date "2023-01-01" @default.
- W4386014987 modified "2023-10-02" @default.
- W4386014987 title "The Application of Machine Learning on Concrete Samples" @default.
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- W4386014987 doi "https://doi.org/10.1007/978-3-031-40398-9_5" @default.
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