Estimating compressive strength of high-performance concrete using different machine learning approaches

The assessment of compressive strength in high-performance concrete (HPC) holds significant importance, both in practical applications and in the context of its challenging mix proportions design, which often demands numerous experimental trials to achieve the desired property such as compressive strength. This process is typically resource-intensive and time-consuming. However, notwithstanding the complexities of its design, artificial intelligence (AI) techniques have demonstrated efficacy in accurately predicting desired concrete properties by optimizing mixture proportions. This research proposes AI-based models to predict the compressive strength of HPC. In this study, twenty-six different models with six approaches, namely linear regression, regression tress, support vector machine, ensembles of trees, gaussian process regression, and neural network models, were employed. In addition, different K-folds and optimizers were used. The models, based on 152 experimental results, utilized 10 variables, including the content of cement, slag, silica fume, water, superplasticizer, fine aggregate, and coarse aggregate, in addition to the moisture content of the fine and coarse aggregates, and concrete age as inputs to predict HPC's compressive strength. 80 % of the data was utilized for training, while the remaining 20 % for testing. Model performance was assessed using R2 and RMSE, MSE, and MAE. For the testing data, empirical results from the performance evaluation revealed that cubic support vector machine model using 8-k fold and grid search optimizer with principal component analysis had the worst performance, while fine regression tree model using 2-k fold and Bayesian optimization had relatively superior performance with highest correlation and least error (R2 = 0.94, RMSE = 4.15, MSE = 17.279, and MAE = 3.133) in comparison with the other developed models.