Multifunctional Models, Including an Artificial Neural Network, to Predict the Compressive Strength of Self-Compacting Concrete

In this study, three different models were developed to predict the compressive strength of SCC, including the nonlinear relationship (NLR) model, multiregression model (MLR), and artificial neural network. Thus, a set of 400 data were collected and analyzed to evaluate the effect of seven variables that have a direct impact on the CS, such as water to cement ratio (w/c), cement content (C, kg/m(3)), gravel content (G, kg/m(3)), sand content (S, kg/m(3)), fly ash content, (FA, kg/m(3)), superplasticizer content (SP, kg/m(3)), and curing time (t, days) up to 365 days. Several statistical assessment parameters, such as the coefficient of determination (R-2), root mean squared error (RMSE), mean absolute error (MAE), and scatter index (SI), were used to assess the performance of the predicted models. Depending on the statistical analysis, the median percentage of superplasticizers for the production of SCC was 1.33%. Furthermore, the percentage of fly ash inside all mixes ranged from 0 to 100%, with 1 to 365 days of curing and sand content ranging from 845 to 1066 kg/m(3). The results indicated that ANN performed better than other models with the lowest SI values. Curing time has the most impact on forecasts for the CS of SCC modified with FA.

Automated summary

In this study, three different models were developed to predict the compressive strength of SCC. A set of 400 data was collected and analyzed to evaluate the effect of seven variables on CS. The results showed that the artificial neural network model performed the best in terms of prediction performance and curing time had the greatest impact on the forecast for FA-modified SCC.