Various simulation techniques to predict the compressive strength of cement-based mortar modified with micro-sand at different water-to-cement ratios and curing ages

Advances in technology and environmental issues enable the building industry to use ever more high-performance materials. In this analysis, the hardness of cement mortar with high-volume silica fume has been evaluated and modeled using different model technics. Cement is an essential component of building construction. Physical and mechanical properties of cement mortar, as well as an appropriate design, are accountable for building mechanical strength. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of cement mortar containing a high volume of silica fume (SF) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested cement mortar modified with SF) from different academic research studies have been statically analyzed and modeled. For that purpose, linear and nonlinear regression, M5P tree, and artificial neural network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of cement mortar are silica fume (SF) incorporation ratio (0–50% of cement’s mass), water-to-cement ratio (0.235–1.2), and curing ages (1 to 180 days). According to the correlation coefficient (R), mean absolute error (MAE), and root mean a square error (RMSE), the compressive strength of cement mortar can be well predicted in terms of w/c, silica fume, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the nonlinear regression, ANN, and M5P tree models seem to be the most reliable models. The results of this study suggest that the nonlinear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of cement mortar with this data set.