Predicting parameters and sensitivity assessment of nano-silica-based fiber-reinforced concrete: a sustainable construction material

Abstract

This study evaluates the compressive strength (C–S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed advanced ML approaches used for the analysis are Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and eXtreme Gradient Boosting (XGB). Furthermore, the level of accuracy for the employed advanced algorithms is also evaluated by the k-fold cross-validation technique. Statistical checks, i.e., root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE), are also applied to validate the performance of algorithms. Sensitivity analysis is also made to explore the influence of input parameters on the C–S of NS-FRC. Among all, the XGB technique is found most effective for an accurate C–S prediction of NS-FRC. In XGB model, the coefficient of determination (R2) is 0.95, which is comparatively more than that of SVM (0.90) and MLP (0.90). The MAE value of XGB algorithm is 3.3 MPa which is lower than that of SVM (4.8 MPa) and MLP (4.5 MPa). In addition, RMSE value is also less for XGB algorithm (3.8 MPa) as compared to that of SVM (5.5 MPa) and MLP (5.9 MPa). Furthermore, the employed XGB models exhibited highest R2 of 0.95 as compared to the models reported in the available literature. The sensitivity analysis revealed that the nano-silica influenced the C–S of NS-FRC by 7%. Moreover, discussion reveals that nano-silica in concrete can have several benefits, such as improved microstructure, enhanced strength, prolonged durability, reduced cement content, and less carbon emission.