Paper Contents
Abstract
Machine learning, a subset of artificial intelligence, enables computers to learn from data and make predictions based on identified patterns within large datasets. This study explores the potential of machine learning models in classifying the compressive strength of concrete specimens with diverse ingredient compositions. While existing literature has investigated estimating concrete density, there is a notable absence of studies focused on categorizing compressive strength. To address this gap, three machine learning classification algorithmsDecision Tree, Naive Bayes Classifier, and K-Nearest Neighborsare employed to classify concrete samples. The study evaluates and compares the performance of each algorithm.The findings reveal that the Decision Tree classifier outperforms the other algorithms, achieving an average precision and recall of 99%, an f1-score of 0.99, and an accuracy of 99%. This study not only establishes the effectiveness of machine learning in concrete strength classification but also provides valuable insights into the practical application of machine learning algorithms using a real-world dataset. The results underscore the potential of machine
Copyright
Copyright © 2024 V E S Mahendra Kumar. This is an open access article distributed under the Creative Commons Attribution License.
