Research Article
Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming
Table 7
Results of model 1 (ANN) and model 2 (GP).
| Model 1: | Training of the dataset | Artificial Neural Network (ANN) | Epochs taken | Coefficient of determination () | Root mean square error (RMSE) | Result number | Curing time |
| R1 (without fly ash) | 28 days | 04 | 0.898 | 6.9762e − 006 | 56 days | 05 | 0.998 | 1.2712e − 007 | 91 days | 03 | 01 | 7.3640e − 009 | R2 (with 0.15 fly ash) | 28 days | 05 | 0.996 | 3.8809e − 007 | 56 days | 04 | 01 | 3.6873e − 009 | 91 days | 04 | 01 | 2.2181e − 010 |
| Model 2: Genetic Programming (GP) |
| | 28 days | Not applicable | 0.77438 | 0.01067 | R3 (without fly ash) | 56 days | 0.99999 | 0.00550 | | 91 days | 0.99999 | 0.00644 | | 28 days | 0.93781 | 0.01415 | R4 (with 0.15 fly ash) | 56 days | 0.94483 | 0.00910 | | 91 days | 0.96681 | 0.00689 |
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