Advances in Materials Science and Engineering / 2023 / Article / Tab 9 / Research Article
A Neural Network-Based Prediction of Superplasticizers Effect on the Workability and Compressive Characteristics of Portland Pozzolana Cement-Based Mortars Table 9 Coefficient of correlation values of target variables.
Target variables Training Validation Testing Combination Compressive strength at 1st day 0.98647 0.98514 0.93245 0.95632 Compressive strength at 3rd day 0.99235 0.98745 0.96210 0.97230 Compressive strength at 7th day 0.99412 0.98213 0.95021 0.95542 Compressive strength at 14th day 0.96520 0.96541 0.97854 0.94021 Compressive strength at 28th day 0.90014 0.98814 0.98206 0.91566 Slump flow at 0 min 0.91254 0.97654 0.96542 0.93654 Slump flow at 30 min 0.92541 0.96521 0.97541 0.96541 Slump flow at 60 min 0.93650 0.99754 0.96742 0.98785 Slump flow at 90 min 0.99851 0.98631 0.98745 0.99856 Slump flow at 120 min 0.94632 0.99883 0.98322 0.95120
