Forecasting the strength of micro/nano silica in cementitious matrix by machine learning approaches

Abstract

This work provides an intelligent machine learning-based technique for anticipating cement paste compressive strength incorporating nanocomposite. In this approach, artificial neural network (ANN), gene expression programming (GEP), random forest (RF), and ensemble approaches are used to combine multiple weak learners capable of identifying the mapping between input and output data into a robust learner. The accuracy of the strong learner as a whole will be increased by incorporating the weak learner with the least prediction error with enhanced correlation. The components of the cement paste mixture (such as water-to-cement ratio, nano-silica (%), micro-silica (%), curing time (days), and the compressive strength value are used as input data and output data. Thus, 205 datasets of cement compressive strength tests are collected to train and assess the learners. The reliability of the models was evaluated using cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE). Therefore, interpretable methods using AdaBoost provide a significant correlation with a lower error rate of R2 = 0.96. The two that perform the best are ANN with ensemble Adaboost and RF with an R-square of 0.97. Additionally, the cross-validation findings demonstrate that the answer was correct and had minimal mistakes. The ensemble model, therefore, displays a strong connection. In addition, new expressions for the predictions of the 28-day compressive strength of nanocomposite containing silica content are developed based on the database. Sensitivity analysis reveals that curing time and silica content has a major influence on prediction. In addition, a model capable of predicting the values of the desired outputs when the necessary input parameters are provided is trained to generate the graphical user interface (GUI). It simplifies the procedure and provides a useful instrument for employing the model's civil engineering abilities.