Artificial Intelligence for Predicting the Concrete’s UCS based on Experimental Data

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Mahmodi Hamed; Samadzamini Karim

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Journal Paper

Paper Information

Journal: Civil and Geoengineering Letters Year:2024 | Volume:1 | Issue:2 Page(s): 1-7

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Information Journal Paper

Title

Artificial Intelligence for Predicting the Concrete’s UCS based on Experimental Data

Author(s)

Mahmodi Hamed | Samadzamini Karim | Issue Writer Certificate

Keywords

Artificial intelligence

Uniaxial compressive strength

Concrete prediction

MLP

Neural networks

Abstract

This study investigates the application of Artificial intelligence in predicting the Uniaxial compressive strength of concrete using experimental data. A multilayer perceptron (MLP) neural network was developed using TensorFlow and Keras in Python program. The dataset includes 150 concrete cube samples tested after 28-days of curing, divided into training (70%) and testing (30%) sets. The results show that the proposed model significantly improves prediction accuracy, achieving over 80% recall and maintaining consistent performance through 500 iterations, with an accuracy range of 80-85%. Comparative analysis with algorithms such as SVM, k-NN, RF, DT, and Adaboost indicates that the MLP model provides superior accuracy in predicting concrete strength. The confusion matrix reveals 91% accuracy and 99% precision. Evaluation using MSE, MAE, and RMSE metrics confirms that the MLP model has lower error rates than other algorithms, demonstrating its effectiveness in predicting Uniaxial compressive strength.

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