Prediction of Concrete's Compressive Strength via Artificial Neural Network Trained on Synthetic Data
Received: 25 October 2023 | Revised: 8 November 2023 | Accepted: 11 November 2023 | Online: 2 December 2023
Corresponding author: Saleh J. Alghamdi
Abstract
Predicting concrete compressive strength using machine learning techniques has attracted the focus of many studies in recent years. Typically, given concrete mix ingredients, a machine learning model is trained on experimental data to predict properties of hardened concrete, such as compressive strength at 28 days. This study used computer-generated mix design data that contained mixed ingredients along with the corresponding theoretical strength of each mix to train a neural network and then test them on real-world experimental data. The developed model was able to predict the compressive strength of concrete specimens at 28 days with an R-value of 0.80. Furthermore, increasing the synthetic dataset increased the performance of the model to a point beyond which it started to decrease. The proposed sustainability-promoting method emphasizes the effectiveness of using synthetic data to train machine learning models that yield insightful predictions with acceptable accuracy.
Keywords:
concrete, compressive strenght, synthetic data, computer-generated data, AI, machine learningDownloads
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