Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete

Table 1

Descriptive statistics of the experimental data.

Input parameter	Min	Max	Mean	Standard deviation	Median	Range	Standard error	Average deviation
Cement	110	600	349.22	93.43	337.5	490	6.53	72.48
Limestone powder	0	272	25.67	60.78	0	272	4.25	41.82
Fly ash	0	440	106.36	94.01	110	440	6.57	78.60
GGBS	0	330	17.39	52.01	0	330	3.63	30.19
Silica fume	0	250	14.91	33.45	0	250	2.34	22.16
RHA	0	200	6.55	24.29	0	200	1.70	11.88
Coarse aggregate	500	1600	772.35	175.36	768.88	1100	12.25	124.16
Fine aggregate	336	1135	827.93	144.33	836	799	10.08	105.95
Water	94.5	250	179.27	27.65	180	155.5	1.93	20.49
SP	0	22.5	5.96	4.35	5.97	22.5	0.30	3.33
VMA	0	1.23	0.14	0.31	0	1.23	0.02	0.22