Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Table 6

Metrics results of the different regression methods.

Dataset	Metric	RF	SVMLin	SVMPoly	PLS	Bagging	DT	ANN	FL
D112_fc	R	0.916	0.912	0.920	0.907	0.915	0.857	0.932	0.945
	RMSE	2.362	2.518	3.046	2.604	2.419	2.878	2.855	1.090
	MAE	1.957	1.837	2.423	2.001	2.117	2.511	2.625	0.933
D112_S	R	0.833	0.758	0.761	0.705	0.705	0.693	0.897	0.947
	RMSE	4.748	4.983	5.094	5.380	6.100	5.942	2.686	2.477
	MAE	4.302	3.702	3.933	4.476	5.776	5.465	3.409	1.954
D224_fc	R	0.853	0.816	0.816	0.779	0.736	0.408	0.899	0.928
	RMSE	2.054	3.285	2.943	3.243	2.689	3.008	2.107	1.442
	MAE	1.641	2.678	2.316	2.724	2.192	2.364	2.926	0.995
D224_S	R	0.772	0.654	0.765	0.645	0.730	0.518	0.896	0.977
	RMSE	3.778	5.114	4.005	5.015	4.094	5.424	2.534	1.413
	MAE	2.428	3.994	2.708	4.050	3.254	4.442	3.842	1.152