Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete

AGRAWAL, Achal; CHANDAK, Narayan

doi:10.2478/acee-2024-0014

Figures & Tables

ANFIS network designed for the SCC strength modeling [28]

Training phase results of ANFIS for CS28

Training phase results of ANFIS for TS28

SVM parameters used in the present study [20]

SVM Parameter	Adoption/Values
Kernel function	RBF
Scaling factor	1
Method	Quadratic programming
Support Vectors	13x5
Bias	-0.012

Correlation matrix for TS28

Variables	SP	PC	PPF	SF	SFD	SFT	VF	TS28
SP	1.00	0.14	0.00	0.00	0.42	−0.36	−0.32	0.71
PC	0.14	1.00	0.02	0.02	0.36	−0.36	−0.44	0.69
PPF	0.00	0.02	1.00	−0.01	−0.67	0.70	0.66	0.21
SF	0.00	0.02	−0.01	1.00	−0.30	0.23	0.31	0.29
SFD	0.42	0.36	−0.67	−0.30	1.00	−0.98	−0.96	0.26
SFT	−0.36	−0.36	0.70	0.23	−0.98	1.00	0.97	−0.23
VF	−0.32	−0.44	0.66	0.31	−0.96	0.97	1.00	−0.25
TS28	0.71	0.69	0.21	0.29	0.26	−0.23	−0.25	1.00

Performance Matrix for CS28

Models	Index of Agreement (IOA)	Akaike Information Criterion (AIC)	Skill Score (SS)	Symmetric Uncertainty (SU)
ANFIS	0.50	256.34	0.50	0.01
ANN	0.64	232.34	0.64	0.29
SVM	0.96	68.33	0.96	0.93
MLR	0.51	256.04	0.51	0.01
GEP	0.62	159.31	0.58	0.73

Performance Matrix for TS28

Models	Index of Agreement (IOA)	Akaike Information Criterion (AIC)	Skill Score (SS)	Symmetric Uncertainty (SU)
ANFIS	0.67	−59.96	0.67	0.33
ANN	−0.54	50.01	−0.54	−2.07
SVM	0.81	−99.25	0.81	0.61
MLR	0.87	−127.64	0.87	0.74
GEP	0.62	−51.32	0.62	0.25

Correlation matrix for CS28

VARIABLES	SP	PC	PPF	SF	SFD	SFT	VF	CS28
SP	1.00	0.14	0.00	0.00	0.42	−0.36	−0.32	0.68
PC	0.14	1.00	0.02	0.02	0.36	−0.36	−0.44	0.72
PPF	0.00	0.02	1.00	−0.01	−0.67	0.70	0.66	0.16
SF	0.00	0.02	−0.01	1.00	−0.30	0.23	0.31	0.32
SFD	0.42	0.36	−0.67	−0.30	1.00	−0.98	−0.96	0.28
SFT	−0.36	−0.36	0.70	0.23	−0.98	1.00	0.97	−0.25
VF	−0.32	−0.44	0.66	0.31	−0.96	0.97	1.00	−0.26
CS28	0.68	0.72	0.16	0.32	0.28	−0.25	−0.26	1.00

Initial values of ANFIS GA parameters [28]

Parameters	Values
Population size	20
Iterations	1000
Crossover rate	0.70
Mutation rate	0.50
Inversion rate	0.10
Selection pressure	8.0
Gamma	0.20

Dataset for training and testing of different models

SP (kg)	PC (kg)	PPF (kg)	SF (kg)	SFD (Dia)	SFT (Sec)	VF (Sec)	CS28 (MPa)	TS28 (MPa)	Water (kg)	FA (kg)	CA1 20-10 mm (kg)	CA2 10-4.75 mm (kg)
9	600	0	0	780	2	7	36.11	4.41	240	810	365	365
9	600	1.5	3	665	5	13	39.95	4.79	240	810	365	365
9	600	3	6	630	6	14	41.1	5.26	240	810	365	365
9.225	615	0	0	790	1.5	6	39.55	4.67	246	810	365	365
9.225	615	1.535	3.07	730	3	9	42.33	5.66	246	810	365	365
9.225	615	3.07	6.15	690	4	11	46.31	6.12	246	810	365	365
9.45	630	0	3.15	750	2.5	8	45.52	6.03	252	810	365	365
9.45	630	1.575	6.3	730	3	8	49.16	6.88	252	810	365	365
9.45	630	3.15	0	725	3	9	46.46	6.28	252	810	365	365
12	600	0	6	760	2	8	44.12	5.86	240	810	365	365
12	600	1.5	0	765	2	7	41.68	5.53	240	810	365	365
12	600	3	3	710	4	12	45.09	6.22	240	810	365	365
12.3	615	0	3.07	775	2	7	48.03	6.74	246	810	365	365
12.3	615	1.535	6.15	725	3	9	53.11	7.26	246	810	365	365
12.3	615	3.07	0	710	4	10	47.36	6.67	246	810	365	365
12.6	630	0	6.3	810	1	6	57.16	7.8	252	810	365	365
12.6	630	1.575	0	785	2	6	54.3	7.52	252	810	365	365
12.6	630	3.15	3.15	750	3	9	56.44	7.76	252	810	365	365
3.3	565	0	0	690	2	11	52.8	3.9	186	750	58.5	391.5
5	566.7	0	0	680	2.2	16	57.3	4	170	760	58.5	391.5
5.1	566.7	0	39	665	2.8	18	56.9	5.9	170	760	58.5	391.5
5.3	566.7	4.55	39	670	3.1	19	61.7	6.9	170	760	58.5	391.5
5.7	566.7	6.825	39	660	3.3	18	58.8	7.2	170	760	58.5	391.5
6.2	566.7	9.1	39	645	4.2	20	56.7	6.9	170	760	58.5	391.5
10	500	0	0	700	2.4	7	37.21	4.25	190	1005	310	310
10	500	0	157	600	4	12	51.31	7.2	190	1005	310	310
10	500	0	117.75	620	3.8	11	50.24	6.25	190	1005	310	310
10	500	0	78.5	640	3.5	10.5	50.45	6.1	190	1005	310	310
10	500	0	39.25	650	3.5	10.5	45.64	4.8	190	1005	310	310
10	500	0	0	670	3	8.3	34.87	4.1	190	1005	310	310
10	500	0.455	157	580	5	12.7	40.52	5.8	190	1005	310	310
10	500	0.455	117.75	600	4	12	45.78	5.8	190	1005	310	310
10	500	0.455	78.5	620	3.5	11	50.44	6.25	190	1005	310	310
10	500	0.455	39.25	630	3.5	10.6	47.86	5.85	190	1005	310	310
10	500	0.455	0	640	3.2	10.5	47.84	5.75	190	1005	310	310
10	500	0.91	157	560	5.5	13	64.1	7.25	190	1005	310	310
10	500	0.91	117.75	570	5.5	12.5	54.98	6.15	190	1005	310	310
10	500	0.91	78.5	590	5	11.5	47.64	5.7	190	1005	310	310
10	500	0.91	39.25	610	4.5	11	46.09	5.75	190	1005	310	310
10	500	0.91	0	620	4	11	43.96	3.8	190	1005	310	310
10	500	0	0	720	2.2	6.8	39.04	4.3	190	1005	310	310
10	500	0	157	620	4	11.6	66.69	7.95	190	1005	310	310
10	500	0	117.75	630	4	11	53.91	6.5	190	1005	310	310
10	500	0	78.5	640	3.5	10.5	52.31	6.5	190	1005	310	310
10	500	0	39.25	660	3.5	10.5	47.96	5.7	190	1005	310	310
10	500	0	0	680	2.5	8.1	39.53	4.6	190	1005	310	310
10	500	0.455	157	600	4.5	12.5	52.27	6.45	190	1005	310	310
10	500	0.455	117.75	620	4	12	62.67	7.35	190	1005	310	310
10	500	0.455	78.5	630	4	11	56.92	6.5	190	1005	310	310
10	500	0.455	39.25	640	3.5	10.4	55.13	6.45	190	1005	310	310
10	500	0.455	0	650	3.5	10.2	53.21	6.2	190	1005	310	310
10	500	0.91	157	580	5.5	12.5	69.12	8.1	190	1005	310	310
10	500	0.91	117.75	590	5.5	12	55.68	6.8	190	1005	310	310
10	500	0.91	78.5	600	5	11.8	50.05	6.4	190	1005	310	310
10	500	0.91	39.25	620	4	11	46.5	5.9	190	1005	310	310
10	500	0.91	0	630	4	10.6	46.05	4.4	190	1005	310	310
10	500	0	0	740	2	6.8	42.83	4.95	190	1005	310	310
10	500	0	157	630	4	11.6	68.14	8.05	190	1005	310	310
10	500	0	117.75	630	4	11	64.79	7.5	190	1005	310	310
10	500	0	78.5	650	3.2	10.5	56.55	6.65	190	1005	310	310
10	500	0	39.25	660	3	10.5	49.82	5.9	190	1005	310	310
10	500	0	0	700	2.5	8.1	46.72	4.75	190	1005	310	310
10	500	0.455	157	620	4.5	12.5	60.22	7	190	1005	310	310
10	500	0.455	117.75	630	4	12	69.71	8.15	190	1005	310	310
10	500	0.455	78.5	640	4	11	64.52	7.4	190	1005	310	310
10	500	0.455	39.25	650	3.5	10.4	56.7	6.6	190	1005	310	310
10	500	0.455	0	670	3	10.2	53.73	6.55	190	1005	310	310
10	500	0.91	157	600	5	12.5	69.79	8.15	190	1005	310	310
10	500	0.91	117.75	610	5	12	56.67	7.75	190	1005	310	310
10	500	0.91	78.5	620	4.5	11.8	52.48	6.8	190	1005	310	310
10	500	0.91	39.25	640	3.5	11	49.6	6.15	190	1005	310	310
10	500	0.91	0	650	3.5	10.6	49.4	5	190	1005	310	310

GEP parameters [27]

Parameters	Values
Population size	30
Genes per chromosome	3
Gene head length	9
Functions	+, -./, *,^
Gene tail length	12
Mutation rate	0.05
Inversion rate	0.1
Gene transposition rate	0.1
One-point recombination rate	0.3
Two-point recombination rate	0.3
Gene recombination rate	0.1
Fitness function	R≥0.7

Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete

Figures & Tables

Figure 1.

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Figure 7.

Figure 8.

Figure 9.

Figure 10.

SVM parameters used in the present study [20]

Correlation matrix for TS28

Performance Matrix for CS28

Performance Matrix for TS28

Correlation matrix for CS28

Initial values of ANFIS GA parameters [28]

Dataset for training and testing of different models

GEP parameters [27]

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