Akademik Veri Yönetim Sistemi
7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023, İstanbul, Türkiye, 23 - 25 Kasım 2023
In this study, we present an ANOVA (Analysis of Variance) method conducted on a geopolymer dataset. The analysis focuses on evaluating the impact of various input variables, including OB, GW, FA, S, M, M/B, AGE, HEAT, SiO2, Al2O3, Na2O, K2O, Fe2O3, CaO, MgO, SO3, TiO2, BaO, Mn3O4, Cr2O3, SrO, P2O5, V2O5, ZrO2, L.O.I., on the outcome variables FS and CS. The study calculates F-statistics and associated p-values for each input variable, which measure the significance of their effects on FS and CS. Results indicate which input variables have a statistically significant impact on the outcome variables, aiding in the identification of key factors influencing geopolymer strength and quality. The paper also presents tabulated results, highlighting variables with significant effects and those with lesser influence. These findings offer valuable insights for optimizing geopolymer production and quality control processes. The input variable FA has an Fstatistic of 3.259 with a small p-value of 0.020, indicating it has a statistically significant impact on CS. This variable plays a notable role in determining geopolymer strength. Input variables like Al2O3 (F = 2.261, p = 0.070) also exhibit a significant influence on CS. For FS dataset, among the input variables analyzed, only FA appears to have a statistically significant effect on the geopolymer's final strength, while the others are not found to be statistically significant in influencing FS. Input variables such as OB, GW, S, M, M/B, AGE, HEAT, SiO2, Al2O3, Na2O, K2O, Fe2O3, CaO, MgO, TiO2, BaO, Cr2O3, SrO, P2O5, V2O5, ZrO2, and L.O.I. have high p-values (typically greater than 0.05), indicating that they have a less statistically effect on FS in this analysis.