Volume 1, Issue 2 — Year 2024 — Article e100014

ISSN (Online): 3115-8129 Biannually

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

Article Type: Research
Pages: e100014
DOI: https://doi.org/10.22034/CGEL.1.2.e100014
Crossmark
Open Access
Authors
Affiliations
  1. Department of Civil Engineering, University Collage of Nabi Akram (UCNA), Tabriz 5183919611, Iran
  2. Department of Computer Engineering, University Collage of Nabi Akram (UCNA), Tabriz 5183919611, Iran
Corresponding author
Email: samadzamini@ucna.ac.ir
ORCID: 0009-0004-2799-9747
Received: 11 September 2024 / Accepted: 10 December 2024 / Published: 21 December 2024
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.

Keywords

Artificial intelligence, Uniaxial compressive strength, Concrete prediction, MLP, Neural networks

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • MLP predicts concrete strength with high accuracy
  • Model outperforms SVM and other algorithms
  • Low error metrics confirm prediction reliability
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How to cite
Mahmodi, H., & Samadzamini, K. (2024). Artificial Intelligence for Predicting the Concrete’s UCS based on Experimental Data. Civil and Geoengineering Letters, 1(2), e100014. https://doi.org/10.22034/CGEL.1.2.e100014
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