Multi-response optimization using Taguchi-Grey relational analysis for composition of fly ash-ground granulated blast furnace slag based geopolymer concrete

Prusty, Jnyanendra Kumar; Pradhan, Bulu

doi:10.1016/j.conbuildmat.2020.118049

Cited by 124 publications

(47 citation statements)

References 52 publications

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“…Biomass wastes such as rice husk ash and palm oil fuel ash have been recycled as sources [ 6 , 7 ]. Other common sources are metakaolin [ 8 , 9 ], fly ash [ 10 ], and ground granulated blast-furnace slag (GBBS) [ 11 ]. Geopolymer coating using RHA as the aluminosilicate source has shown excellent fire retardant properties.…”

Section: Introductionmentioning

confidence: 99%

Rice Husk Ash/Silicone Rubber-Based Binary Blended Geopolymer Coating Composite: Fire Retardant, Moisture Absorption, Optimize Composition, and Microstructural Analysis

et al. 2021

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Geopolymer coating using rice husk ash (RHA) as the aluminosilicate source has shown excellent fire retardant properties. However, incorporation of rice husk ash into the geopolymer matrix increased water absorption properties of the polymer composite. As such, silicone rubber (SiR) was introduced to improve the moisture absorption and fire retardant properties of the composite. Additionally, the less efficient one-factor-at-a-time (OFAT) approach was conventionally used in past studies on the RHA-based geopolymer composite. In understanding the optimum value and significant effect of factors on the fire retardant and moisture absorption properties of the binary blended geopolymer coating composite, the use of statistical analysis and regression coefficient model (mathematical model) was considered essential. The objectives of this study are to identify the significant effect of factors on moisture absorption and fire retardant properties, to determine the optimum composition, and to study the microstructure of the rice husk ash/silicone rubber (RHA/SiR)-based binary blended geopolymer coating composite. The RHA/AA and SiR/Ge ratios were chosen as factors, and the response surface methodology (RSM) was employed to design experiments and conduct analyses. Fire retardant and moisture absorption tests were conducted. A scanning electron microscope (SEM) was used to observe the microstructure of geopolymer samples. The RHA/alkaline activator (AA) and SiR/Ge ratios were shown to have a significant effect on the responses (temperature at equilibrium and moisture absorption). The high ratio of RHA/AA and SiR/Ge resulted in a lower temperature at equilibrium (TAE) below 200°C and at moisture absorption below 16%. The optimum formulation for the geopolymer coating composite can be achieved when the RHA/AA ratio, SiR/Ge ratio, and sodium hydroxide concentration are set at 0.85, 0.70, and 14 M, respectively. SEM micrographs of samples with good fire retardant properties showed that the char residue of the geopolymer composite coating, which is a layer of excess silicone rubber, is porous and continuous, thus providing a shielding effect for the layer of geopolymer underneath. The sample with good moisture absorption showed the formation of a thin outer layer of silicone rubber without any cracks. The unreacted SiR formed a thin layer beneath the geopolymer composite matrix providing a good moisture barrier.

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Section: Introductionmentioning

confidence: 99%

Rice Husk Ash/Silicone Rubber-Based Binary Blended Geopolymer Coating Composite: Fire Retardant, Moisture Absorption, Optimize Composition, and Microstructural Analysis

et al. 2021

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“…Despite the level of successes recorded with the utilization of the Taguchi optimization method, it still exhibits the inherent limitation of single response optimization associated with every other DOE method. In order to circumvent this drawback when confronted with multiple response problems, the Taguchi method is usually integrated into some numerical and analytical techniques to achieve multiple response optimization [39][40][41][42][43][44][45][46]. However, most of the developed numerical and analytical techniques for Taguchi multiple response optimization present modest results due to their inherent shortcomings [47], which poses a huge setback to the wide acceptance of the Taguchi optimization method.…”

Section: Taguchi Optimization Methodsmentioning

confidence: 99%

Variable returns to scale DEA—Taguchi approach for ternary additives optimization in expansive soil subgrade enhancement

Ikeagwuani

Nwonu

2021

Geo-Engineering

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In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach to optimize ternary additives for expansive soil enhancement. The ternary additives selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). The additives were set as the input variables while multiple responses obtained from the experiments performed with the Taguchi orthogonal array were set as the output variables. Each row in the orthogonal array were defined as a decision making unit (DMU) in the optimization process and output-oriented VRS model was used to obtain the efficiency score for each DMU. Next, benevolent formulation was utilized to obtain the multipliers for the inputs and outputs which were subsequently used to determine the cross efficiency scores for each DMU. The cross-efficiency scores were used to construct the cross-efficiency matrix. Thereafter, the mean cross-efficiency score (MCES) was determined for each DMU. Parameter level that maximizes the MCES was chosen as the optimal level for that parameter. Optimum combination of additives was found at A6 B2 C3. Lastly, confirmatory experiments performed by blending the soil with the optimum combination of additives showed the effectiveness of this method in the enhancement of expansive soil properties.

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“…The Taguchi optimization method is a method preferred to design parameters with a minimum number of tests and samples [25]. Besides, the combination of optimum parameters can be obtained after analysis using the Taguchi method [26]. There are many studies about design and estimation with the Taguchi method in different research fields.…”

Section: Introductionmentioning

confidence: 99%

Priz Hızlandırıcı Katkılı Betonun Mekanik ve Dayanıklılık Özelliklerinin Taguchi Optimizasyonu

Çöğürcü

Uzun

2022

Politeknik Dergisi

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Multi-response optimization using Taguchi-Grey relational analysis for composition of fly ash-ground granulated blast furnace slag based geopolymer concrete

Cited by 124 publications

References 52 publications

Rice Husk Ash/Silicone Rubber-Based Binary Blended Geopolymer Coating Composite: Fire Retardant, Moisture Absorption, Optimize Composition, and Microstructural Analysis

Rice Husk Ash/Silicone Rubber-Based Binary Blended Geopolymer Coating Composite: Fire Retardant, Moisture Absorption, Optimize Composition, and Microstructural Analysis

Variable returns to scale DEA—Taguchi approach for ternary additives optimization in expansive soil subgrade enhancement

Priz Hızlandırıcı Katkılı Betonun Mekanik ve Dayanıklılık Özelliklerinin Taguchi Optimizasyonu

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