Machine learning prediction and optimization of compressive strength for blended concrete by applying ANN and genetic algorithm

Figures & data

Figure 1. Flow diagram for random forest regression.

Figure 2. Flow diagram for AdaBoost regression.

Figure 3. Experimental plan.

Table 1. Limits of each feature used to prepare the concrete.

Features	Cement	Alccofine	GGBS	Water	Days
Unit	kg/m^3	kg/m^3	kg/m^3	liters	–
Min.	261.5	0.00	0.00	140.00	7
Max.	350.00	35.00	52.50	142.00	28

Figure 4. Correlation matrix.

Table 2. Statistical characteristics of experimental data.

Variable	Cement	Alccofine	GGBS	Water	Days	CS
Count	90	90	90	90	90	90
Mean	286.90	23.63	39.38	140.99	16.33	37.74
Std	25.24	10.45	14.85	0.81	8.78	6.93
Min	261.50	0.00	0.00	140	7	22.60
25%	262.50	17.50	35.00	140	7	30.60
50%	280.00	26.25	43.75	141	14	39.70
75%	297.50	35.00	52.50	142	28	42.80
Max	350.00	35.00	52.50	142	28	49.00

Figure 5. Methodology for development of ML model.

Figure 6. Compressive strength of observed and predicted against testing sample of (a) random forest regression and (b) AdaBoost regression.

Table 3. Training and testing measures of regression models.

Metrics	Random forest regression		AdaBoost regression
	Training	Testing	Training	Testing
MAE	0.77	1.47	1.31	1.41
MAPE	0.02	0.04	0.03	0.04
MSE	1.26	3.49	2.89	3.13
RMSE	1.12	1.87	1.70	1.77
R^2	0.97	0.95	0.94	0.92

Figure 7. Comparison of performance metrics of (a) training and (b) testing for random forest regression and AdaBoost regression.

Figure 8. Comparison of prediction error plots for (a) random forest regression and (b) AdaBoost regression.

Figure 9. Comparison of residual plots for (a) random forest regression and (b) AdaBoost regression.

Figure 10. Comparison of feature importance for (a) random forest regression and (n) AdaBoost regression.

Figure 11. Integration of ANN and GA.

Figure 12. ANN architecture between input and response.

Figure 13. ANN model for compressive strength.

Figure 14. Factors of importance (%) on CS.

Data availability statement

The data that support the findings of this study are available from the corresponding author, [C Vivek Kumar], upon reasonable request.