Machine learning approaches for forecasting compressive strength of high-strength concrete

Abstract

Abstract Identifying the mechanical properties of High Strength Concrete (HSC), particularly compressive strength, is critical for safety purposes. Concrete compressive strength is determined by using laboratory experiments, which are costly and time-consuming. Artificial intelligence (AI) methods reduce time and money. This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. The dataset used for the models was obtained from original experimental tests. Important parameters, namely cement content, silica fume, water, superplasticizer, sand, gravel, and curing age, were taken as input to predict the output, which was the compressive strength. Various regression models were investigated for the prediction of outcome compressive strength. To optimize the models, hyperparameters were tuned, and measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared were used for evaluation. XGBoost ( R 2 ≈ 0.94) outperformed other models, demonstrating ML’s potential for HSC strength prediction and demonstrated that Python can be successfully applied to establish accurate and reliable prediction models.