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

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

Table 2.

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

Table 3.

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

ANFIS network designed for the SCC strength modeling [28]

Table 4.

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

Matrix 1.

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

Matrix 2.

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

Training phase results of ANFIS for CS28

Training phase results of ANFIS for TS28

Table 5.

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

Table 6.

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

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/acee-2024-0014 | Journal eISSN: 2720-6947 (formerly 1899-0142) | Journal ISSN: 1899-0142

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Language: English

Page range: 69 - 86

Submitted on: Oct 25, 2023

Accepted on: Jan 25, 2024

Published on: Jan 10, 2025

Published by: Silesian University of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Artificial Intelligence,

Compressive strength,

Support vector machines,

Self-Compacting Concrete,

Tensile strength

Related subjects:

Architecture and design,

Architecture,

Architects, buildings

© 2025 Achal AGRAWAL, Narayan CHANDAK, published by Silesian University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 17 (2024): Issue 2 (June 2024)

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

Figures & Tables

Table 1.

Table 2.

Table 3.

Figure 1.

Figure 2.

Table 4.

Figure 3.

Matrix 1.

Matrix 2.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Table 5.

Table 6.

Paradigm

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