AN ANN MODEL FOR PREDICTING SLUMP OF CONCRETE CONTAINING CRUSHED GLASS AND NATURAL GRAVEL

Year 2024, , 43 - 53, 30.06.2024

Bala Alhaji Abbas , Sulaimon Nurudeen Adisa , Jibrin Abubakar , Bello Abeeb Akorede , Alao Sodiq Alabi

https://doi.org/10.47137/uujes.1396383

Abstract

This study modelled the slump of concrete containing crushed glass and Bida Natural Gravel (BNG) based on deep learning algorithm using the MATLAB neural network toolbox. A total of 240 (150mm × 150mm × 150mm) cubes were cast from 80 mixes generated randomly using Scheffe’s simplex lattice approach. Slump was measured for each of the experimental points of fresh concrete before filling in the moulds. The resulting batch for each mix was used as input data while the laboratory results for slump was used as output data for the ANN-model. Hence a shallow multilayer supervised Neural Network was developed to model these data. The developed model would be able to predict concrete slump containing 0% - 25% crushed glass as partial replacement for fine aggregate, water- cement ratio ranging from 0.45 – 0.65 and concrete grade M15 – M25. The architecture of the network contained 6 input parameters: water to cement ratio, water, cement, sand, crushed glass and BNG, 20 neurons in the hidden layer and slump in the outer layer. The adequacy of the developed model was measured using Mean Square Error (MSE) and Correlation Coefficient (R). Results showed that 6:20:1 model architecture for slump model had an MSE values for training, validation and testing as: 1.84e-2, 5.81e-3, 3.64e-3, 1.73e-3 respectively. Regression values for training, validation and testing are: 79%, 94%, 96% and 79%. The study concluded that a shallow multilayer Neural Network architecture with 20 neurons in the hidden layer is sufficient for predicting concrete slump.

Keywords

BP-ANN, Bida Natural Gravel (BNG), crused glass, mean square error (MSE), Regression, slump

References

• Yeh IC. Modeling slump flow of concrete using second-order regressions and artificial neural networks, Cement and Concrete Composites, 2007;29(6):474-480
• Vinay C, Vinay A, Ravindra N, and Sarbjeet S. Modeling slump of ready mix concrete using artificial neural network, International Journal of Technology, 2015;2:207-216.
• Pandelea A, Budescu MG, and Covatariu GM. Applications of artificial neural networks in civil engineering. In Proceedings of the Second International Conference for PhD Students in Civil Engineering and Architecture: 2014.
• Chine WH, Chen L, Hsu HH, and Wang TS. Modelling slump of concrete using artificial neural networks. In Proceedings of International Conference on Artificial Intelligence and Computational Intelligence: 2010; Sanya, China;2010: 236-239.
• Oztas A, Pala M, Ozbay E, Kanca E, Caglar N, and Bhatti MA. Predicting the compressive strength and slump of high strength concrete using neural network, Construction and Building Materials, 2006; 20(9):769-775.
• Bai J, Wild S, Ware JA and Sabir AA. (2003). Using neural networks to predict workability of concrete incorporating metakaolin and fly ash, Advances in Engineering Software, 2003;34(11): 663-669.
• Yeh IC. Exploring concrete slump model using artificial neural networks, Journal of Computing in Civil Engineering, 2006;20(3):217-221.
• Sadiqul GM, Islam MH, and Rahman NK. Waste glass powder as partial replacement of cement for sustainable concrete practice, International Journal of Sustaianable Built Envioronment, 2017;6: 37–44.
• Wang X, and Luan Y. Modelling of hydration, strength development, and optimum combinations of cement-slag-limestone. Ternary Concrete International Journal of Concrete Structures and Materials,2018;12:1-13.
• Zainab ZI and Enas A. Recycling of waste glass as a partial replacement for fine aggregate in concrete. Elsevier Journal: Waste Management, 2008;29:655–659
• Shayan A, and Xu A. Value-added utilization of waste glass in concrete. Cement and Concrete Research,2004;34(1):81–89.
• Sobolev K, Türker P, Soboleva S, and Iscioglu G. Utilization of waste glass in ECO-cement: strength properties and microstructural observations. Waste Management, 2006;27(7):971–976
• Rakshvir M, and Barai SV. Studies on recycled aggregates-based concrete. Waste Management & Research, 2006;24(3):225–233
• Corinaldesi V, Gnappi G, Moriconi G and Montenero A. Reuse of ground waste glass as aggregate for mortars, Waste Management, 2005;25(2):197–201.
• Chen CH, Wu JK, and Yang CC. (2006). Waste e-glass particles used in cementitious mixtures, Cement and Concrete Research, 2006;36(3):449–456.
• Metwally I. Investigations on the performance of concrete made with blended finely milled waste glass, Advances in Structural Engineering, 2007;10 (1):47–53.
• Scheffe H. Experiments with mixture, Journal of the Royal Statistical Society, Ser B, 1958;20:344 – 360.
• Jung HC, and Jamshid G. Genetic algorithm in structural damage detection. Computers and Structures, 2001;79(30):1335- 53.
• Pala M, Ozbay E, Oztas A, and Ishak YM. (2005). Appraisal of long-term effect of fly ash and silica fume on compressive strength of concrete by neural networks, Construction and Building Materials, 2005;21(2):384-394.
• Constantinou NR. Assessment of the compressive strength of concrete using artificial neural networks, Postgraduate work in Applied Computational Structural Engineering, Department of Educational Civil Engineering of the School of Pedagogical & Technological Education, Athens, Greece, 2017.
• BS 1008. Specification for sampling, testing and assessing the suitability of mixing water for concrete, London: British Standard Institution; 2002.
• BS 812-103.1. Methods for determination of particle size distribution, London: British Standard Institution; 1985.
• BS 1881-102. Method for determination of Slump test value of concrete, London: British Standard Institute; 1983