Volume 1, Issue 2 — Year 2024 — Article e100024

Ali Golsoorat Pahlaviani¹; Sina Aminbakhsh²

1. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1955847781, Iran
2. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1955847781, Iran

This study utilizes a support vector machine (SVM) supervised learning algorithm to predict the uniaxial compressive strength (UCS) of lightweight concrete (LWC). Implemented in Python 3, the model leverages a dataset of 120 samples of LWC, which was divided into training and testing sets in a 70%-30% split. Key performance indicators, including mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), were assessed for both sets. A confusion matrix was also generated to evaluate the classification accuracy. The predictive modeling results show that the SVM algorithm achieved an accuracy of 83.63% and a precision of 85.11% in predicting the UCS of LWC samples. The calculated error metrics were promising, with MAE, MSE, and RMSE values at 0.346, 0.329, and 0.331, respectively. These findings suggest that the SVM model is capable of accurately predicting UCS for lightweight concrete, demonstrating its potential for application in concrete strength assessment. As results, the SVM model offers an efficient predictive approach for estimating UCS in lightweight concrete, providing valuable insights for material testing and construction planning. This work highlights the utility of machine learning in advancing accuracy and reliability in civil engineering materials science.

Machine learning, SVM, Concrete, Strength prediction, Uniaxial compression, Prediction