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By: M.M Chokhawala, Jaydeep J. Chavda, J. R. Pitroda, Indrajit N. Patel, and Chirag Patel.
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2. J. Chavda, K. Vaghela ,and J. Pitroda,“ Bacterial approach for enhancing concrete strength: areview, ”Studies in Indian Place Names(SIPN),vol.40, Issue 50, pp. 4590-4604, March 2020.
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5. J. Chavda, K. Vaghela, and J. Pitroda, “An experimental investigationon high strength concrete incorporated with bacteria,” Journal ofEmerging Technologies and Innovative Research (JETIR), vol. 7, Issue5, pp. 750-758, May 2020.
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11. M. Ahmed, K. Hadi, M. Hasan, J. Mallick and A. Ahmed, “Evaluatingthe co-relationship between concrete flexural tensile strength andcompressive strength,” Int. J. Structural Engineering, vol. 10, pp. 1-17,July2012.
Concrete plays a key role as a construction material across the globe. The strength parameters of the concrete are primary reason for which it is designed for. The design mixed concrete has multiple strength parameters like compressive strength, spilt tensile strength, flexural (bending) strength etc. For each newly design mixed concrete, the mentioned strength parameters are tested for various time duration like 3 days, 7 days, 14 days, 28 days etc. before using the prepared concrete for actual work. This process may consume additional project duration along with the additional materials. One can adopt prediction modelling concept to overcome the issue as in prediction modelling multiple results can be predicted based on one obtained result. By doing so one can reduce project duration along with material wastage. In this paper statistical regression analysis is used for the strength prediction purpose using Microsoft excel 2013 and the accuracy was obtained using deviation factor. Experimental tests were conducted on M40 concrete specimens to determine their mechanical properties. The collected data was statistically analyzed, and regression models were formulated using five different equations, namely (i) linear, (ii) exponential, (iii) logarithmic, (iv) polynomial and (v) power equations incorporating time duration as key influencing factor in strength properties of concrete. The emphasizes on the relation between flexural strength, split tensile strength and compressive strength of M40 grade concrete. Multiple regression models were obtained using multiple regression equations and most precise regression model was identified based on its deviation factor value.
Keywords: Compressive strength, Flexural strength, Split tensile strength, M40 concrete, Multiple regression analysis, Predictive modeling
