Prediction of fracture toughness of concrete using the machine learning approach

Abstract

In the process of structural design, it is useful to estimate the fracture toughness of concrete samples. This research showcases the effectiveness of utilizing machine learning methods to determine the fracture toughness of concrete. Taking into account variables such as mix design, machine learning techniques can accurately predict the mode I fracture toughness of concrete. Dimensionless stress intensity factor of concrete prediction using twelve different machine learning techniques namely, Linear regression (LR), Extreme Gradient-Boosting (XGboost), K-Nearest Neighbors (KNN), Random Forest (RF), Category Boosting (CB), Decision Tree (DT), Extra Trees (ET), Light Gradient-Boosting (LightGB), Adaptive boosting (AdaBoost), Bagging (BA), Gaussian Process (GP), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The result of utilizing the training adaptive moment estimation algorithm has been developed to create an outstanding machine learning-based system. After carefully analyzing comparisons between the predictions produced by different models and experimental findings, it has been discovered that the models demonstrate an impressive accuracy rate of about 90 percent when it comes to forecasting concrete fracture toughness. The research findings emphasize that the ANN model exhibited superior accuracy in its predictions (R2 value of 0.90, RMSE of 0.1517, and MAE of 0.1238). Upon conducting a thorough examination of the ANN method’s sensitivity, the cement parameter holds utmost significance in accurately estimating concrete’s fracture toughness using the available dataset. So, the ANN model can be used as a valuable method to provide practical assistance in predicting the fracture toughness of concrete. When evaluating the effective parameters, the cement and metakaolin dosage and the notch height to specimen height ratio have the greatest effect on the fracture resistance, while the coarse aggregate content is minimal. This result is consistent with the experimental data.