Optimization of colloidal nano-silica based cementitious mortar composites using RSM and ANN approaches

Abstract

Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete.