Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Muthukumar, Soundar Rajan; Jegatheeswaran,

doi:10.1590/1517-7076-RMAT-2023-0247

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Articles • Matéria (Rio J.) 29 (1) • 2024 • https://doi.org/10.1590/1517-7076-RMAT-2023-0247 copy

Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

When the only information available is the issue parameters, and the intended outwards, machine learning techniques like ANN (Artificial Neural Networks) and ANFIS (Adaptive neuro-fuzzy inference system) been proven to address the complex problems without duplicating the phenomena under investigation. The main prompting characteristics are the height-to-the-thicknesses ratio of prisms and the strength under compression of prisms and mortar were analyzed. As inputs, the prototypes are used as blocks and mortars. Both prototypes were accomplished and evaluated. Thirty-six data sets were gathered for testing in addition to verified technical and subsequently comparison with other empirical computation methods served to validate. The outwards show that the suggested prototypes have good forecast capabilities with negligible error rates. To assess and compare the structural behavior of structural completion of AAC block with the other types. At last, both the machine learning tools are good application and dependability.

Keywords
ANN; ANFIS; AAC block masonry prism; statistical model values

DESCRIPTION	HEIGHT TO THICKNESS RATIO	f_block (MPa)	f_mortar (MPa)	f_prism (MPa)
AACB1 M1	4.3	4.46	6.35	2.33
AACB2 M1	4.3	4.53	6.39	2.38
AACB3 M1	4.3	4.58	6.42	2.26
AACB1 M2	4.3	4.46	10.04	2.95
AACB2 M2	4.3	4.53	10.12	2.79
AACB3 M2	4.3	4.58	10.18	3.02
AACB1 M3	4.3	4.46	18.66	3.55
AACB2 M3	4.3	4.53	18.75	3.63
AACB3 M3	4.3	4.58	18.81	3.72

VARIABLE	INFORMATION USED FOR DEVELOPING AND EVALUATING ANN AND ANFIS PROTOTYPES [²¹[21] AYDIN, A.C., TORTUM, A., YAVUZ, M., “Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference system”, Civil Engineering and Environmental Systems, v. 23, n. 4, pp. 295–309, 2006. doi: http://dx.doi.org/10.1080/10286600600772348 https://doi.org/10.1080/1028660060077234... ]			STANDARD DEVIATION	COV (%)
VARIABLE	LOWEST	HIGHEST	MEAN	STANDARD DEVIATION	COV (%)
height to thickness ratio	2.00	5.00	3.00	4.58	0.50
f_block (MPa)	9.00	40.00	22.00	35.12	0.61
f_mortar (MPa)	4.00	27.00	13.00	12.77	0.90
f_prism (MPa)	6.00	30.00	18.00	27.65	0.83

ROLE OF LAYER	NAME OF THE LAYER	NODE EQUATION
Fuzzy Layer	I Layer	$O_{i}^{1} = μ_{A i} (x)$
Rule Layer	II Layer	$O_{i}^{2} = μ_{A i} (x) \times μ_{B i} (y)$
Normalization Layer	III Layer	${\bar{w}}_{i} = \frac{w_{i}}{(w_{1} + w_{2})}$
Defuzzification Layer	IV Layer	$O_{i}^{4} = {\bar{w}}_{i} f_{i}$
Output Layer	V Layer	$O_{i}^{5} = \frac{\sum_{i} w_{i} f_{i}}{\sum_{i} w_{i}}$

EFFICIENCY MEASURES		MEAN SQUARE ERROR (MSE)	MEAN ABSOLUTE PERCENTAGE ERROR (MAPE)	INTEGRAL ABSOLUTE ERROR (IAE)	COEFFICIENTS (R²)
ANN	Practice Set	0.4523	0.0842	4.12%	0.9547
ANN	Examining Set	0.6622	0.0285	4.59%	0.9382
ANFIS	Practice Set	0.3287	0.0738	3.35%	0.9663
ANFIS	Examining Set	0.4249	0.0256	3.66%	0.9536

PERFORMANCE	NOTATIONS	COMPUTED EQUATIONS
Mean Square Error	MSE	$\frac{1}{p} \sum_{j} {(o_{j} - t_{j})}^{2}$
Coefficient	R ²	$1 - (\frac{\sum_{j} {(o_{j} - t_{j})}^{2}}{\sum_{j} {(o_{j} - \bar{o})}^{2}})$
Mean Absolute Percentage Error	MAPE	$\frac{1}{p} \sum_{j} \|\frac{o_{j} - t_{j}}{o_{j}}\|$
Integral Absolute Percentage Error	IAE	$\frac{{\sum_{j} [{(o_{j} - t_{j})}^{2}]}^{0.5}}{\sum_{j} o_{j}} \times 100 %$

PROPOSED REFERENCE	EXPRESSION	MEAN	STANDARD DEVIATION
MANN [⁴⁴[44] MANN, W., “Statistical evaluation of tests on masonry by potential functions”, In: Proceedings of the 6th International Brick Masonry Conference, Rome, Italy, 1982.]	$f = 0.83 \times f_{b}^{0.67} \times f_{m}^{0.33}$	1.19	0.22
DAYARATNAM [⁴⁵[45] DAYARATNAM, P., Brick and reinforced brick structures, New Delhi, Oxford & IBH Pub., 93 p., 1987.]	$f = 0.28 \times f_{b}^{0.50} \times f_{m}^{0.50}$	0.32	0.22
KAUSHIK et al. [⁴⁶[46] KAUSHIK, H.B., RAI, D.C., JAIN, S.K., “Stress-strain characteristics of clay brick masonry under uniaxial compression”, Journal of Materials in Civil Engineering, v. 19, n. 9, pp. 728–739, 2007. doi: http://dx.doi.org/10.1061/(ASCE)0899-1561(2007)19:9(728) https://doi.org/10.1061/(ASCE)0899-1561(... ]	$f = 0.63 \times f_{b}^{0.49} \times f_{m}^{0.32}$	0.48	0.34
Eurocode 6 [³⁴[34] BRITISH STANDARDS INSTITUTION, BS EN 1996 (Eurocode 6): design of Masonry structures, London, BSI, p. 128, 2005.]	$f = K \times f_{b}^{0.65} \times f_{m}^{0.25}$	0.57	0.34
Design proposal	ANN Architecture	1.02	0.38
Design proposal	ANFIS Architecture	1.11	0.42

Laboratório de Hidrogênio, Coppe - Universidade Federal do Rio de Janeiro, em cooperação com a Associação Brasileira do Hidrogênio, ABH2 Av. Moniz Aragão, 207, 21941-594, Rio de Janeiro, RJ, Brasil, Tel: +55 (21) 3938-8791 - Rio de Janeiro - RJ - Brazil
E-mail: revmateria@gmail.com

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[1] Corresponding Author: Soundar Rajan Muthukumar, e-mail: soundarrajan06@gmail.com, profdjpillai@gmail.com