Sah, A.K.; Hong, Y.-M. Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction. Materials2024, 17, 2075.
Sah, A.K.; Hong, Y.-M. Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction. Materials 2024, 17, 2075.
Sah, A.K.; Hong, Y.-M. Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction. Materials2024, 17, 2075.
Sah, A.K.; Hong, Y.-M. Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction. Materials 2024, 17, 2075.
Abstract
This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, and Regression Tree—are employed and compared for performance. Evaluation metrics such as Mean Absolute Deviation, Root Mean Square Error, Coefficient of Correlation, and Mean Absolute Percentage Error are used. After preprocessing 1030 samples, the dataset is split into 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.
Keywords
Concrete compressive strength; Regression tree; Artificial neural network (ANN); Root mean square error; Coefficient of Correlation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
