Application of the response surface method to optimize alkali activated cements based on low-reactivity ladle furnace slag

Pinheiro, Claver; Rı́os, Sara; Fonseca, António Viana da; Fernández-Jiménez, Ana; Cristelo, Nuno

doi:10.1016/j.conbuildmat.2020.120271

Cited by 45 publications

(12 citation statements)

References 44 publications

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“…Accordingly, in recent years, numerous mathematical predictions and optimisation models have been developed within the research community, including artificial neural networks (ANNs) [ 25 , 26 , 27 ], metaheuristic algorithms [ 28 , 29 ], genetic expression programming (GEP) [ 30 , 31 , 32 , 33 ], adaptive neuro-fuzzy inference systems (ANFIS) [ 34 , 35 , 36 ], and response surface methodology (RSM) [ 37 , 38 , 39 ]. Among them, RSM is one of the best statistical techniques used for data optimization [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R2 and adj. R2 was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.

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Section: Introductionmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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“…where Y is the anticipated UCS response, X i and X j are the points of autonomous factors x i and x j , β 0 is the intercept, β i , β ii and β ij are respectively the linear, quadratic and interaction coefficients, and ε is the associated random error (Pinheiro et al, 2020).…”

Section: Experimental Design and Statistical Analysismentioning

confidence: 99%

Strength Analysis and Optimization of Alkali Activated Slag Backfills Through Response Surface Methodology

Dai

Ren

et al. 2022

Front. Mater.

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The significant difference in water-to-binder ratio, activator type and concentration between alkali-activated slag (AAS) paste/mortar/concrete and AAS-based cemented paste backfill (AAS-CPB) means that previous results related to the properties and mix optimization of AAS materials cannot be directly translated to AAS-CPB. This study statistically identifies the effect of key influential variables such as silicate modulus, slag fineness and activator concentration on 3- and 28 day unconfined compressive strength (UCS) of AAS-CPB by central composite design (CCD) established in response surface methodology (RSM). In this study, the prominence of independent variables and their relations are investigated by using ANOVA (analysis of variance) having a significant level of 0.05. ANOVA results certify that there is a strong link between the level of variable contribution on UCS performance of AAS-CPB and curing age. Obviously, silicate modulus and activator concentration are the most major variables influencing UCS at 3 and 28 days, respectively. Increased fineness of slag and higher pH of pore solution enhance 3 day UCS, but restrain the further hydration of unreacted slag and subsequent the gain in strength at advanced curing ages. The combination of independent variables of silicate modulus (0.295), slag fineness (12630.2), activator concentration (0.45) gives the optimum responses.

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“…Te test results became concise and easy to understand. Tis statistical method has been studied in mixed proposition optimization designs [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Study on Proportion Optimization of Magnesium Oxychloride Cement-Stabilized Clayey Soil Based on the Response Surface Methodology

Zhang

Wang

et al. 2023

Advances in Materials Science and Engineering

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The energy-saving and green environmental protection magnesium oxychloride cement (MOC) is introduced into the pavement base and subbase to improve the shortcomings of CO2 emissions and high industrial energy consumption in the production process of traditional cementitious materials such as lime and Portland cement. Box−Behnken design of design-expert is employed for experiment arrangement, in which MgO/MgCl2 molar ratio, MOC content, and fly ash content are influencing factors, while response values are 7d unconfined compressive strength (USC) and 1d softening coefficient (SC) of solidified soil. The response surface methodology (RSM) is used to optimize the ratio of three additives, and the effects of various factors on the response value are investigated by response surface model analysis and interaction analysis. The results show that the MOC content has the most excellent effect on 7d unconfined compressive strength, and the mutual influence for the MOC content and fly ash content are significant, respectively. However, the influential factor is the fly ash content for the 1d softening coefficient. It is predicted by the RSM analysis that the optimum balance of USC and SC is 8.61 for the MgO/MgCl2 molar ratio, 18% for the MOC content, and 20.36% for the fly ash content. With the additives in the optimal ratio, the actual unconfined compressive strength and softening coefficient of stabilized soil are 2.56 MPa and 0.76, respectively. It is confirmed that the response surface methodology plays an important part in optimizing the proportion of MOC-stabilized clayey soil.

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Application of the response surface method to optimize alkali activated cements based on low-reactivity ladle furnace slag

Cited by 45 publications

References 44 publications

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Strength Analysis and Optimization of Alkali Activated Slag Backfills Through Response Surface Methodology

Study on Proportion Optimization of Magnesium Oxychloride Cement-Stabilized Clayey Soil Based on the Response Surface Methodology

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