Prediction of Compressive Strength of Concrete containing Nanosized Cassava Peel Ash as partial Replacement of Cement using Artificial Neural Network
Abstract
The leaping impact of increased population and commercialization on global energy demand, has prompted more concern for sustainable development and strength evaluation of concrete structures. This study was carried out to improve the strength of concrete by adopting nanosized cassava peel ash (NCPA) as partial replacement of cement and to model its strength with artificial neural network (ANN). Data used for the model were obtained experimentally. At any percentage not exceeding 20 % NCPA replacement, the concrete is a suitable structural material. The neural network was adequately trained to capture the relationship between the compressive strength values of NCPA-concrete and their corresponding mix ratios at 7 days, 14 days, 28 days, 56 days, 90 days and 150days curing. A 6-10-1 network architecture was created. A total of four hundred (400) training data set were presented to the network. Two hundred and forty (240) of these were used for training the network, sixty (60) were used for validation, and another sixty (60) were used for testing the network's performance. After training the network, the output and targets had an R - value of 0.99909 which is very close to 1. This shows that the data used for training the network, have a good fit. The results obtained from the network are approximately the same as that obtained experimentally. The adequacy of the network was further tested using the Student’s T test. The calculated T-value (-0.11) for the compressive strength of NCPA-concrete was less than that from the T-table (2.04) at 95% confidence level, proving that the network predictions are reliable. This model is reliable, time-effective and accurate for strength prediction of nanosized concrete.References
Abdulwahab, M.T., Uche, O.A.U. (2021). “Durability Properties of Self- Compacting Concrete (SCC) Incorporating Cassava Peel Ash (CPA)”. Nigerian Journal of Technology (NIJOTECH), 40(4), pp.584-590. http://dx.doi.org/10.4314/njt.v40i4.4.
ASTM C 618 (2008). “Specification for coal fly ash and raw or calcined natural pozzolanas for use as mineral admixtures in Ordinary Portland Cement Concrete”, Annual book of ASTM standards, West Conshecken, USA.
Arum, C (2008), “Verification of Properties of Concrete Reinforcement Bars: Nigeria as a Case Study”, Indoor and Built Environment, 17(4), pp.370-376.https://doi.org/10.1177/1420326x08094264.
Awodiji, C.T.G, Onwuka, D.O, Okere, C.E, Ibearugbulem, O.M (2018), “Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model”. Civil Engineering Journal, 4(12), pp. 3005-3018. Doi: http://dx.doi.org/10.28991/cej- 03091216.
Awoyera, P.O., Kirgiz, M. S., Viloria, A., Ovallos-Gazabon, D. (2020), “Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques”, Journal of Materials Research and Technology, 9 (4), pp. 9016-9028. https://doi.org/10.1016/j.jmrt.2020.06. 008.
Baikerikar, A. (2014), A Review on Green Concrete, Journal of Emerging Technologies and Innovative research, 1(6), pp. 472-474.
Bourchy, A., Barnes, L., Bessette, L., Chalencon, F., Joron, A., and Torrenti, J. M. (2019), Optimization of Concrete Mix Design to Account for Strength and Hydration Heat in Massive Concrete Structures, Cement and Concrete Composites, 103, pp. 233- 241.https://doi.org/10.1016/j.cemconc omp.2019.05.005.
BS 3140 (1980). “Methods of Test for Water for Making Concrete, Including Notes on the Suitability of the Water”, British Standard Institute, BSI-London.
BS 882 (1992). “Specification for aggregates from natural sources for concrete”, British Standard Institute, BSI- London.
BS 12 (1996) “Specification for Portland cement”, British Standard Institute, BSI-London.
BS 1881(1983) “Testing Concrete: Method for Determination of Compressive Strength of Concrete Cubes”. British Standard Institute, BSI- London.
Fausett L (1994). Fundamentals of Neural Network. New York. Prentice-Hall, pp. 289-295.
Khan, S. U, Nuruddin, M. F., Ayub, T., Shafiq, N. (2014), “Effects of Different Mineral Admixtures on the Properties of Fresh Concrete”, The Scientific World Journal, 986567, pp. 1-11. https://doi.org/10.1155/2014/986567.
Khan, S. U., Ayub, T., Rafeeqi, S. F. A. (2013), Prediction of compressive strength of plain concrete confined with ferrocement using Artificial Neural Network (ANN) and comparison with Existing Mathematical Models, American Journal of Civil Engineering and Architecture, 1(1), pp. 7-14. DOI: 10.12691/ajcea-1-1-2.
Nuruddin, M. F, Demie, S, Shafiq, N. (2011), “Effect of mix composition on workability and compressive strength of self-compacting geopolymer concrete”. Canadian Journal of Civil Engineering, 38, pp. 1196–203, doi: http://dx.doi.org/10.1139/l11-077.
Nwa-David, C. D., Onwuka, D. O, Njoku, F. C., Ibearugbulem, O. M. (2023), “Prediction of Fresh and Hardened Properties of Concrete Containing Nanostructured Cassava Peel Ash Using Ibearugbulem's Approach”, Engineering and Technology Journal, 41(5), pp.1-14, http://doi.org/10.30684/etj.2023.13809 9.1374.
Nyarko, M. H., Nyarko, E. K., Ademović, N., Miličević I. and Šipoš T. K. (2019), Modelling the Influence of Waste Rubber on Compressive Strength of Concrete by Artificial Neural Networks, Materials, 12(4), pp. 561- 569.
Obi, K.O. and Adinna, O.B. (2023), “Strength and Workability of Concrete with Inclusion of Fly Ash, Quarry Dust and a Generic Naphthalene-Based Superplasticizer”, FUOYE Journal of Engineering and Technology (FUOYEJET), 8(1), 115-119. http://doi.org/10.46792/fuoyejet.v8i1. 984.
Ojeda, J.M.P., Bocanegra, S. R., Huatangari, L. Q. (2021), “Determination of the Compressive Strength of Concrete Using Artificial Neural Network”, International Journal of Engineering and Technology Innovation, 11(3), pp. 204-215. https://doi.org/10.46604/ijeti.2021.747 9.
Olajumoke, A. M., Oke, I. A., Fajobi, A. B. and Ogedengbe, M. O. (2009), “Engineering Failure Analysis of a Failed Building in Osun State, Nigeria”. Journal of Failure Analysis and Prevention, 9 (1), pp. 8-15. https://doi.org/10.1007/s11668-008- 9197-7.
Olonade, K.A., Olajumoke, A.M., Omotosho, A.O., and Oyekunle F.A. (2014), “Effects of sulphuric acid on the compressive strength of blended cement cassava peel ash concrete”. Construction Materials and Structures, pp. 764-771. doi:10.3233/978-1- 61499-466-4-764.
Sanchez, F., Sobolev, K. (2010), Nanotechnology in concrete – A review, Construction and Building Materials, 24, pp. 2060–2071. doi: 10.1016/j.conbuildmat.2010.03.014.
Sebastia, M., Fernandez, O. I, and Irabien, A. (2003), Neural network prediction of unconfined compressive strength of coal fly-ash cement mixtures. Cement and Concrete Research, 33(8), pp. 1137–1146. https://doi.org/10.1016/S0008- 8846(03)00019-X.
Thandavamoorthy, T. S (2015), Determination of concrete compressive strength: A novel approach, Advances in Applied Science Research, 6(10), pp. 88-96
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