“Optimize the Mixed Proportions of Geopolymer Concrete to achieve Desired Strength by using RSM Method”

Abstract

GGBFS-FA-based GPC offers a clean and sustainable development technology alternative. In this study, the RSM method was used to optimize the mixed proportions of geopolymer concrete to achieve desired strength criteria. Four factors and four levels were considered: binder content, including four combinations of FA and GGFBS dosage, dosage of super plasticizer (0.5, 1.0, 1.5 and 2%), Na2SiO3/NaOH ratio (1.5, 2.0, 2.5 and 3), and molarity (6, 8, 10 and 12). Using these ingredients and factors, the effect of compressive strength was examined. The RSM approach using an L16 orthogonal array was employed to find the optimum condition of every factor while limiting the number of experiments. The findings indicated that the optimum synthesis conditions for maximum compressive strength obtained from the binder comprised 45% of FA, 45% of GGBFS and 10% of silica fume, 1.5% dosage of super plasticizer.

Introduction

II. LITERATURE REVIEW

Abiola Adebanjo et.al (2023) in the research paper, Soft computing methods were used to design and model the compressive strength of high-performance concrete (HPC) with silica fume. Box-Behnken design-based response surface methodology (RSM) was used to develop 29 HPC mixes with a target compressive strength of 80±10 MPa.

Cement (450-500 kg/m3 ), aggregates (1500-1700 kg/m3 ), silica fume (SF) (20-45% weight of cement) and water-binder (w/b) ratio of (0.24 - 0.32) were provided as input factors while the compressive strength at 7 and 28 days were analysed as responses. Datasets for the artificial neural network (ANN) prediction were generated from 87 experimental observations from the compressive strength test.

A. S. Srinivasa et.al (2023) research paper reported the work on developing an optimized mix proportion of novel one-part geopolymer (OPG) binder produced by dry blending the solid aluminosilicate precursor and solid alkali source and then adding free water to the blended mix similar to the preparation of Ordinary Portland Cement (OPC). A three-level Box-Behnken Response Surface Method (RSM) design was used to study the properties of OPG mixes at fresh and hardened state and to test and develop the regression models

The detailed experimental programmer design of in this chapter. It covers materials concrete component testing, mix proportioning, experiment details, and test sets, among other things.

III. MATERIALS

1. Cement
2. Sand
3. Aggregate
4. Fly ash
5. Silica Fume (SF)
6. Geopolymer
7. Water

Response surface methodology is a popular mathematical and statistical method for experimental design. The response of interest is affected by several variables, and the objective of this method is to optimize the response. RSM investigates to establish an appropriate relationship between input and output variables and understands the optimal operating condition for a system under research. Or in other words, this technique investigates the effect of the independent variables (Factors) over the response/output, either alone or in combination. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. RSM, being a statistical approach, has been extensively employed to maximize the production of certain substances by optimizing the variables that participate in the operation. Design of Experiments (DOE) has been used extensively for this optimization using RSM.

A. Design Of Experiments, DOE

Design Of Experiment (DOE) is a multipurpose mathematical methodology that has been used for planning and conducting experimental programs. DOE (Design Of Experiments) is a branch of applied statistics that are used to perform scientific studies of a system, process, or product in which input variables (Xs) were manipulated to investigate its effects on measured response variables.

1. In the Engineering and Research environment, experiments are often conducted to explore the relationship between the key input process variables (factors) and the output performance characteristics (that define the quality of the material), estimate the relationship, and confirm. Exploring includes understanding data from the process, whereas estimating refers to determining input variables' effects on the response characteristics. The confirmation step verifies the predicted results obtained from the experiments.
2. One of the very popular scientific methods employed by many engineers until the 19th century was OVAT-one variable at a time. In this method, one variable was varied, keeping all other variables fixed in an experiment. However, this approach was later considered inefficient, unreliable, and time-consuming as this largely depends on other factors such as guesswork, luck, experience, etc.

Conclusion

The responses were empirically modelled as linear function for compressive and flexural and as quadratic functions for tensile. The models were validated using Minitab, and the results showed a high level of accuracy (R2 values between 72.0 and 99.0%). According to the results of the response surface modelling, the optimum mechanical qualities of GGBS concrete can be achieved by combining 30% crumb rubber with 14M sodium hydroxide. GGBS 80+FA 10+ SF 10.

References

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Copyright

Copyright © 2023 Naresh Gulati, Dr. M. P. Verma, Dr. Raghvendra Singh, Ashish Kant Ahirwar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.