DEVELOPMENT OF COMPRESSIVE STRENGTH PREDICTIVE MODELS OF SELF-COMPACTING CONCRETE CURED USING DIFFERENT CURING METHODS
Abstract
Curing is the process of controlling the rate and extent of moisture loss, relative humidity and temperature from newly poured concrete for a certain period of time after it has been cast or finished to ensure that the cement has been properly hydrated and the concrete has hardened. The concrete strength, durability and other physical properties are affected by curing and application of the various types as it relates to the prevailing weather conditions in a particular locality, as curing is one of many requirements for concrete production, as such it is important to study the effect of different curing method. The concrete cube specimens produced with cement, fine aggregate, and coarse aggregate mix-ratio of 1:2.23:1.62 were prepared with a water-cement ratio of 0.5 and superplasticizer (SP) dosages of 0.5%, 1.0%, and 1.5%. The SP dosages were computed as percentages by weight of the cement content. The cubes were tested for compressive strength after curing for 7, 14, 21, 28, and 56 days using three curing methods namely; Immersion, open air, and wet burlap curing methods. This study assessed the effect of different curing methods on compressive strength of self-compacting concrete through the development of a mathematical method to model and analyze the effect of the curing methods used on the compressive strength of the SCC and also to validate the reliability of the method used. Data Fit software was used in the model development, the curing age and super-plasticizer dosage were used as independent variables while the compressive strength...
References
Ashish, K., and Gaurav, K., (2018). A mix design procedure of self-compacting, international research journal of engineering and technology, ISSN: 2395-0072, volume-5 issue-2, February.
Bashar, S.M. Sani, H. Mohamed, M. and Liew, M.S. (2019). Optimization and characterization of castin-situ alkali-activated pastes by response surface methodology. https://doi.org/10.1016/j.conbuildmat.2019.07.267. DOI: https://doi.org/10.1016/j.conbuildmat.2019.07.267
British Standards Institution. B.S 1881, Part 116, (1985). Method for Determination of Compressive Strength of Concrete Cubes.
Busic, R.; Bensic, M.; Milicevi´c, I.; Strukar, K. (2020). Prediction models for the mechanical properties of self-compacting concrete with recycled rubber and silica fume. Materials 13, 1821. DOI: https://doi.org/10.3390/ma13081821
Gao, Y. Xu, J. Luo, X. Zhu, J. and Nie, L. (2016). “Experiment research on mix design and early mechanical performance of alkali-activated slag using response surface methodology (RSM),” Ceram. Int., vol. 42, no. 10, pp. 11666–11673, doi: 10.1016/j.ceramint.2016.04.076. DOI: https://doi.org/10.1016/j.ceramint.2016.04.076
Haruna, S.; Mohammed, B.; Wahab, M.; Haruna, A. (2020). Compressive Strength and Workability of High Calcium One-Part Alkali Activated Mortars Using Response Surface Methodology. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2020. DOI: https://doi.org/10.1088/1755-1315/476/1/012018
Karim MR, Hossain MM, Elahi MMA, Zain MFM. (2020). Effects of source materials, fineness and curing methods on the strength development of alkali-activated binder. J Build Eng. 29:101147. DOI: https://doi.org/10.1016/j.jobe.2019.101147
Kumator, J. T. Yusuf, D. A. Stephen, P. E., Adamu, L. (2023). Modelling the Permeation Properties of Self-compacting Concrete Incorporating Sporosarcina Pasteurii. Journal of Civil Engineering and Materials Application. doi: 10.22034/jcema.2023.406578.1113.
Mohammed, B.S.; Xian, L.W.; Haruna, S.; Liew, M.; Abdulkadir, I.; Zawawi, N.A.W.A. (2021). Deformation Properties of Rubberized Engineered Cementitious Composites Using Response Surface Methodology. Iran. J. Sci. Technol. Trans. Civ. Eng. 45, 729–740. DOI: https://doi.org/10.1007/s40996-020-00444-3
Murad, N.Y., Imam, R., Abu Hajar, H., Habeh, D., Hammad, A. and Shawash, Z. (2020), "Predictive compressive strength models for green concrete", International Journal of Structural Integrity. Vol. 11 No. 2, pp. 169-184. https://doi.org/10.1108/IJSI-05-2019-0044. DOI: https://doi.org/10.1108/IJSI-05-2019-0044
Nada, A., and Amar, Y.A., (2018). Effect of internal curing on the performance of self-compacting concrete by using sustainable material, matec web of conferences 162, doi.org/10.1051/matecconf/2018/6202017. DOI: https://doi.org/10.1051/matecconf/201816202017
Ogork E. O, Uche O.A.U and Elinwa A., (2014). Strength Prediction Models of Groundnut Husk Ash (GHA) Concrete'‘, American Journal of Civil Structural Engineering, 1(4): p. 104-110. DOI: https://doi.org/10.12966/ajcse.10.03.2014
Okorie, A.U., Sylvia, E. K., Musa, A., Yasser, E. I., Hani, A., & Imhade, P. O., (2022). Modelling and Optimizing the Durability Performance of Self-Consolidating Concrete Incorporating Crumb Rubber and Calcium Carbide Residue Using Response Surface Methodology. https://doi.org/10.3390/ buildings12040398.
Shaikh, A.S., Lahare, P.S., Nagpure, V.B., Ghorpde, S.S., (2017). Curing of Concrete. International Research Journal of Engineering and Technology, Volume: 04 Issue: 03
Sunday, I. (2022). A Developed Predictive Model for Simulating the Effects of Ferrous Ion on the Compressive Strength of Oil Well Integrated Cement Sheath Systems, Using Box–Behnken Design of Experiments. International Journal of Innovative Scientific & Engineering Technologies Research 10(1):9-34.
Tufail, S., Riggs, H., Tariq, M., Sarwat, A.I., (2023). “Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms”, Electronics, v.12, n. 8, pp. 1789. doi: https://doi.org/10.3390/electronics12081789. DOI: https://doi.org/10.3390/electronics12081789
James, T., Malachi, A., E.W. Gadzama, V. Anametemfiok. (2011). Effect of Curing Methods on Compressive Strength of Concrete.Nigerian Journal of Technology Vol. 30, No. 3.
Vivian, W.Y. T., Anthony, B., Khoa, N. L., Luis, C.F. D., Ana, C.J. E. (2022). A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks, Construction and Building Materials Volume 324, https://doi.org/10.1016/j.conbuildmat.2022.126689 DOI: https://doi.org/10.1016/j.conbuildmat.2022.126689
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