Optimum utilization of waste foundry sand and fly ash for geopolymer concrete synthesis using D-optimal mixture design of experiments

Resources, Conservation & Recycling 148 (2019) 114–123

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Optimum utilization of waste foundry sand and fly ash for geopolymer concrete synthesis using D-optimal mixture design of experiments Mayandi Venkatesana, Qammer Zaiba, Izhar Hussain Shaha,b, Hung Suck Parka,

T

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a

Department of Civil and Environmental Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan, 680-749, Republic of Korea Institute of Environmental Sciences and Engineering, School of Civil and Environmental Engineering, National University of Sciences and Technology (NUST) H-12 Campus, Islamabad (44000), Pakistan

b

A R T I C LE I N FO

A B S T R A C T

Keywords: Waste utilization Waste foundry sand (WFS) and fly ash D-Optimal mixture design/response surface methodology/design of experiments Statistical optimization Geopolymer concrete (GPC) Compressive strength

This work examines the partial replacement of fine aggregates with waste foundry sand (WFS) and fly ash, process by-products, to synthesize geopolymer concrete (GPC). D-optimal mixture design of experiments was adopted to guide the proportion of mixture constituents (fine aggregates, WFS, and fly ash) to obtain desired responses (high compressive strengths). The experimentally measured responses/compressive strengths were successfully fitted to Scheffe polynomial model to obtain cubic models which represent compressive strengths of solidified GPC at 7th day curing time (CS 7) and at 28th day curing time (CS 28). The models were statistically evaluated by the standard error of design estimation and experimentally verified by comparing their predicted responses to the independently performed experiments. The models were subjected to analysis of variance (ANOVA) and residuals (diagnostics) for statistical significance and validation, respectively. The established models, hence obtained, were used to assess the impacts of relative proportions of mixture constituents at CS 7 and CS 28. It was observed that, although the highest compressive strength requires a high proportion of fine aggregates, yet, some mixture compositions could be proposed for better utilization of waste materials. Finally, the optimization was performed to maximize the usage of WFS and fly ash. A recipe was identified which yielded 18.9 N/mm² CS 7 and 22.3 N/mm² CS 28 by mere 32 wt. % contribution of fine aggregates in a (fine aggregates + WFS + fly ash) mixture. This study can be helpful in designing experiments and optimizing the utilization of similar waste materials into useful products.

1. Introduction After fresh water resources, sand is considered as the second most consumed natural resource on earth (Edwards, 2015; Gavriletea, 2017). With the ever-increasing demand for construction materials worldwide, it is being depleted at an elevated rate (Bhardwaj and Kumar, 2017). At present, its extensive extraction - around 20 billion tons from waterways and river beds - resulted in the overexploitation of this resource with associated negative impacts (both for natural river water flow and the connected ecosystems) (Edwards, 2015). Such overuse of the natural resource resulted in its scarcity along with an increase in its production cost and environmental degradation (Lohani et al., 2012). Therefore, more attention towards sustainable consumption of this resource is required (Saviour and Stalin, 2012; Xu and Shi, 2018). Sand, as a resource, plays an important role in building and infrastructure construction sector. It is mixed with gravel and Ordinary Portland Cement (OPC) to prepare OPC concrete; the most widely used

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construction material in the world. Each ton of OPC requires approximately seven tons of sand and gravel to produce OPC concrete (Gavriletea, 2017). Unlike the OPC concrete, Geopolymer concrete (GPC) has significant potential for CO2 emission reductions, waste reuse/recycling, and natural resource conservation (Abdollahnejad et al., 2015; Calle et al., 2017; Madhavan and Vijayprakash, 2016; Pan et al., 2011; Provis, 2014). It can be considered as an alternative to OPC concrete with improved environmental sustainability (Duxson et al., 2007; Saha and Rajasekaran, 2017). The term ‘geopolymer’ refers to an amorphous alkali-based aluminosilicate (Singh et al., 2015), with a three-dimensional, highly cross-linked, reticular structural composition (Bernal and Provis, 2014; Sun et al., 2018; Zhang et al., 2018). The composition of GPC may vary according to the end user and it may comprise of fine and coarse aggregates, Waste Foundry Sand (WFS), Low Calcium Fly Ash (LCFA), Ground Granulated Blast Furnace Slag (GGBFS), and alkaline solution. The durability and strength of GPC usually depend upon the type of raw materials and their relative

Corresponding author. E-mail address: [email protected] (H.S. Park).

https://doi.org/10.1016/j.resconrec.2019.05.008 Received 1 October 2018; Received in revised form 10 April 2019; Accepted 7 May 2019 Available online 27 May 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

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proportion (Duxson et al., 2007). Moreover, using waste materials such as fly ash (by-product of thermal power generation) (Provis et al., 2015) and WFS (by-product of iron and steel production) (Bhardwaj and Kumar, 2017; Khatib and Ellis, 2001; Olutoge et al., 2015) can enhance environmental sustainability of their respective production processes. Hence, using the GPC as a sustainable construction material not only helps in minimizing the waste disposal (such as incineration and landfilling etc.) but also reduces the demand for natural virgin resource i.e. sand. This study explores the variability in the compressive strength of GPC upon replacing the sand (fine aggregates) with WFS and fly ash in it. In a concrete, the partial replacement of sand fine aggregates by WFS has been studied for improving its mechanical and microstructural properties (Etxeberria et al., 2010; Gurumoorthy and Arunachalam, 2016; Siddique et al., 2009), for reducing its leachability (Basar and Aksoy, 2012), for maintaining its self-consolidation (Sahmaran et al., 2011), and for improving its abrasion resistance features (Singh and Siddique, 2012). Moreover, the addition of fly ash to a concrete mixture imparts positive impacts on the concrete. It is reported to reduce the settling time, improve early strength (Xu and Shi, 2018), and improve hydration development for pollutant immobilization (Ubbrìaco et al., 2001) in the concrete. The objective of this study is to optimize the utilization of WFS and fly ash in a GPC without critically deteriorating its compressive strength. Statistical experimental design is utilized to comprehend and optimize the impacts of mixture constituents (fine aggregates + WFS + fly ash) on compressive strength of GPC. Response Surface Methodology (RSM) is a statistical technique used for experimental design and is essentially helpful in evaluating the empirical relationship between multiple variables and their respective responses (Anderson and Whitcomb, 2016; Pattanaik and Rayasam, 2018; Zaib and Ahmad, 2019). RSM had been successfully applied for mixture optimization (Cox and Reid, 2000; Eriksson et al., 1998; Muteki et al., 2007) to reduce the number of experimental runs and obtain appropriate predictions (Khan et al., 2017). The D-optimal design is known for its efficiency and feasibility among other RSM designs (such as central composite and Box-Behnken) (Pattanaik and Rayasam, 2018). To determine the optimum mixing proportions for GPC synthesis, Doptimal mixture design was applied. The resulting mixtures were cured and their compressive strengths were experimentally determined. The experimental results were then used to develop models which were statistically evaluated by ANOVA and validated by diagnostics. The models were applied to generate the response surfaces which were evaluated to optimize the desired response i.e. maximum utilization of waste materials to obtain reasonable compressive strengths of GPC. So far, to the best of our knowledge, D-optimal mixture design has not been used for the optimization of GPC compressive strengths with respect to WFS and fly ash compositions. Compressive strength is selected as a response due to its critical importance in the quality of GPC evaluation. Alternative parameters for GPC’s durability analysis may include cracking and shrinkage (Mauroux et al., 2012), carbonation (Turcry et al., 2014), porosity and gas permeability (Hamami et al., 2012), and variability analysis of the material properties (Trabelsi et al., 2011). Although, these parameters are important in assessing the quality of GPC, yet this study is limited to the compressive strength of the GPC at 7th and 28th day curing times (CS 7 and CS 28, respectively).

Table 1 Materials used for GPC synthesis and their procurement sources. Material type

Source

GGBFS WFS Low calcium Class F fly ash

Ulsan Steel Industry Company, Korea Pusan Cast Iron Co., Ltd., Ulsan, Korea Incineration power plant near Ulsan, Korea Local construction material shop in Ulsan, Korea

Fine and coarse aggregates Alkaline activators Sodium hydroxide (NaOH: 98 % purity) Sodium silicate (Na2SiO3)

OCL Company Ltd., Korea Samchun Chemicals, Korea.

taken at 100x and 500x magnification. For the measurement of the compressive strength of the GPC samples, a compression testing machine (CTM JI-108A, Korea) was used. 2.2. GPC synthesis The synthesis of GPC was based on the methodology previously reported (Bakharev, 2006; Vijai et al., 2010) for a given fixed unit weight of 2400 kg/m3 (Table 2) except for the fine aggregate, WFS, and fly ash. The mixing proportions of fine aggregates, WFS, and fly ash were based on D-optimal mixture design and are presented in Table 3. The casting of the specimens was done by pouring the concrete mixture into cylindrically shaped molds of 100 mm × 200 mm (Fig. 1a), followed by hot curing at a temperature of 60–70 °C for 24 h (Fig. 1b). The concrete samples were then kept at room temperature (≤30 °C) for 7–28 days (Fig. 1c). During the curing period, the GPC samples were tested at CS 7 and CS 28 using the compression testing machine (Fig. 1d). Each specimen was compressed until failure to determine its crushing load. The compressive strength of the specimen (CGPC) was computed by using Eq. (1):

CGPC =

Cload EA

(1)

where Cload is the standard compressive strength test for concrete (unit: Newton) and EA refers to the average cross-sectional area (unit: mm2). The compressive strengths were determined by following American Society for Testing and Materials (ASTM) International standard C39. 2.3. Design of experiments Design of experiments is a statistical technique to evaluate linear, non-linear, and interactive effects of independent variables (factors) on dependent variable/s (response) (Anderson and Whitcomb, 2015; Varanda et al., 2017; Zaib et al., 2019; Zaib and Ahmad, 2019). Mixture Table 2 Mixture composition of geo-polymer concrete (GPC). Component

Normal GPC 3

Fine aggregates 554 kg/m Fly ash 275 kg/m3 Foundry sand – Alkaline liquid to fly ash 0.4 ratio Coarse aggregate 1293.4 kg/m3 GGBFS 118.3 kg/m3 NaOH 45.1 kg/m3 Na2SiO3 112.6 kg/m3 Water 136.3 kg/m3 Total solids 455.2 kg/m3 Water to solid ratio 0.3 Unit weight 2400 kg/m3 Fixed GPC constituents are based on (Bakharev, 2006) and (Vijai et al., 2010).

2. Materials and methods 2.1. Materials and instrumentation The materials used for GPC synthesis (fine aggregates, WFS, and fly ash) and their sources are presented in Table 1. The micrographs of the utilized waste materials (WFS and fly ash) and the final product (post solidification GPC) were obtained using a Field Emission-Scanning Electron Microscopy (FE-SEM) (JEOL, JSM-6500A, Japan). The FE-SEM was operated at an accelerating voltage of 15 kV and the images were 115

Proposed GPC (this study) According to D-optimal mixture design

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Table 3 Experimental design for the training and verification of models representing 7th day and 28th day compressive strengths of GPC using various compositions of raw materials. a. Training dataseta Run

Fine aggregates (wt.%)

Waste foundry sand (wt.%)

Fly ash (wt.%)

Exp. value (for training)

Exp. value (for training)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

55.0 55.0 37.8 47.7 30.3 42.5 55.0 34.8 41.5 42.5 48.9 30.3 55.0 41.5

15.0 15.0 33.6 30.5 39.7 27.5 23.3 40.0 38.5 33.3 24.4 39.7 23.3 38.5

30.0 30.0 28.6 21.8 30.0 30.0 21.7 25.2 20.0 24.2 26.7 30.0 21.7 20.0

7th day compressive strength 16.4 18.9 10.0 5.7 13.5 11.6 18.0 14.6 13.6 15.9 9.3 13.6 17.4 15.1

28th day compressive strength 19.9 20.3 12.4 7.4 15.1 14.9 21.4 15.4 16.9 18.5 10.0 15.1 19.9 18.4

Waste foundry sand (wt.%)

Fly ash (wt.%)

Predicted value by model

b. Verification dataset Run

Fine aggregates (wt.%)

Exp. value (for verification)

Differ-enceb (%)

th day compressive strength 1 2 3 4 5 6

55.0 47.7 34.8 42.5 30.3 41.5

15.0 30.5 40.0 33.3 39.7 38.5

30.0 21.8 25.2 24.2 30.0 20.0

RMSE (N/mm²) SEP (%) a b

17.5 6.2 14.7 15.7 13.6 14.2

16.3 5.3 12.4 15.1 13.0 14.3

Exp. value (for verification)

Differ-ence (%)

28th day compressive strength 7.0 14.8 15.9 4.0 4.2 −0.8

1.2 9.4

Predicted value by model

20.1 8.0 15.6 18.4 15.2 17.5

18.9 6.3 14.1 17.3 13.2 16.3

5.9 21.6 9.8 5.8 13.1 6.9

1.5 10.4

based on D-optimal mixture design of experiments. =100*(Predicted-Experimental)/Predicted.

Whitcomb, 2016; Gunst et al., 1996; Myers et al., 2016) is shown in Eq. (2):

design, unlike other design of experiment techniques, requires the sum of input variables (components) to be one. Also, the measured response in a mixture design is independent of the volume of the mixture and depends only on the relative proportions of components in a mixture (Cornell, 2011). In a mixture design, optimal design algorithms evaluate unbiased estimates of the model parameters with the minimal number of experimental runs (Jeirani et al., 2012; Varanda et al., 2017). In this study, all the experiments were designed according to the Doptimal mixture design method. D-optimal mixture design is a prevalent iterative algorithm which minimizes the covariance matrix of the parameter estimates for a certain proposed model (Myers et al., 2016). The method helps in determining the optimal formulation of a mixture, and therefore, provides efficient means of optimizing the synthesis process (Anderson et al., 2018; Arroyo-López et al., 2009; Baek et al., 2017; Pattanaik and Rayasam, 2018). Design Expert 10.0.6 was used in this study to process data for experimental design, empirical models development, analysis of variance, diagnostics, and optimization. The independent variables tested were fine aggregate (A: 30–55 wt.%), WFS (B: 15–40 wt.%), and fly ash (C: 20–30 wt.%) to determine the response of compressive strength (Y: N/mm2) as a dependent variable at 7th and 28th day curing times. Scheffe cubic model, derived from standard polynomials of varying degrees to account for mixture variables, was wielded for the experimental design (Anderson et al., 2018; Provis, 2018). The experimental plan comprised of 14 test runs which included 6 runs for minimum model points, 4 runs for lack of fit estimation, and 4 runs for replicates. The cubic model representing the relationship between independent and dependent variables (Anderson and

Y = β1 A + β2 B + β3 C + β12 AB + β13 AC + β23 BC + β123 ABC + δ12 AB (A − B ) + δ13 AC (A − C ) + δ23 BC (B − C )

(2)

where Y represents response, A, B, and C, are indicative of factors (representing fine aggregate, waste foundry ash, and fly ash, respectively), β 1, β 2, and β 3 denote the coefficients of individual factors, β 12, β 13, β 23, δ 12, δ 13, and δ 23 represent the coefficients of two interacting factors, and β 123 refers to the coefficient of three interacting factors. The two models, one for CS 7 and the other for CS 28, were developed. Table 3 enlists all the experiments, mixture compositions and their corresponding compressive strengths, performed in this study. It shows experimental design matrix based on D-optimal mixture design for the development of the models (training dataset) and their verification (verification dataset). The training dataset consists of fourteen experiments whereas the verification dataset consists of six experiments. The verification experiments were performed at experimental conditions similar to the six of the training data-points (i.e. experimental runs no. 2,4,8,10,12, and 14 of training dataset). The models were evaluated by calculating the percentage difference between models’ predicted values and the experimentally obtained values of the verification experiments. Also, the root mean square error (RMSE) and the standard error of prediction (SEP) were calculated to evaluate the fitting and prediction accuracy of the models (Zafar et al., 2017). Afterwards, analysis of variance (ANOVA) and diagnostics were performed to evaluate the significance of the models (and their terms) and 116

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Fig. 1. Synthesis of GPC samples was carried out in (a) cylindrical molds, (b) cured in a hot chamber at 60–70 °C for 24 h, and (c) stored in a room at < 30 °C temperature before testing the compressive strength using (d) compressive test machine.

shading, at a given coordinate in the factor space, represents lesser error and vice-versa. It can be seen that the errors are relatively small (0.5σ – 0.9σ) and nearly uniform, therefore, the model is expected to provide reasonable predictions (Jeirani et al., 2012; Varanda et al., 2017). The red dots in the standard error plot represent the location of experimental design points. The experimental results of CS 7 and CS 28, for studied proportions of fine aggregates, waste foundry sand, and fly ash in a mixture, are recorded in Table 3. The results were statistically analyzed using ANOVA and the regression equations were developed to capture the effects of three factors (fine aggregates, WFS, and fly ash) on the two responses (CS 7 and CS 28). Eqs. (3) and (4), hence obtained, are empirical models which can be used to predict the CS 7 (Y 7th ) and CS 28 (Y 28th ) :

to validate their assumptions, respectively. Later on, the three dimensional response surfaces (resulting from the interaction of variables in a model) were analyzed. Finally, optimization was performed to locate the response surface regions with the highest desirability. 3. Results and discussion 3.1. Surface morphology The FE-SEM analysis was performed in order to examine the surface morphology of constituent materials (used as a partial replacement for fine aggregates) and the solidified GPC. Fig. 2 shows FE-SEM images of (a) WFS, (b) fly ash, and (c) solidified GPC. The microstructure of WFS has a uniform and smooth surface morphology, while fly ash can be observed to have a gravel-like structure which includes irregular shaped amorphous particles and solid spheres. Post-solidification GPC morphology exhibits a successful coating and binding of WFS and fly ash with adequate infiltration through the fine aggregate particles. This is due to the finely-sized particles present in WFS and GGBFS that effectively form a surface layer over larger particles and fill the inner voids, consequently producing a more homogeneous mixture with reduced surface porosity. The post-solidification change in GPC surface morphology may also be attributed to the overall improvement in concrete structure and its enhanced compressive strength.

Y 7th = 12.45A + 17.60B − 1862.14C − 13.44AB + 3566.93AC + 3234.31BC − 3688.16ABC + 109.99AB (A − B ) − 1999.56AC (A − C ) − 1252.45BC (B − C )

(3)

Y 28th = 13.65A + 20.06B − 2129.57C − 7.48AB + 4088.98AC + 3710.88BC − 4274.71ABC + 134.71AB (A − B ) − 2311.19AC (A − C ) − 1461.99BC (B − C )

(4)

where A, B, and C represent factors i.e. fine aggregates, waste foundry ash, and fly ash, respectively. Eqs. (3) and (4) are in terms of coded factors and, therefore, can be used to predict the responses for given levels of each factor. These equations can be used to compare the relative impacts of the factors by comparing their coefficients. The coefficients represent the level of sensitivity, whereas, the signs represent proportionality (either positive or negative) of the interacting variables to the generated response. The models were experimentally evaluated by plotting the (i) training data and the (ii) verification data (from Table 3) against the perfect prediction line as shown in Fig. 4. The training data consists of the experimental runs employed to develop the models, whereas, the

3.2. Model development The interactions among the constituents of a mixture (fine aggregates, WFS, and fly ash) and their impacts on the compressive strength of the GPC can be described by an empirical model. However, before model development, it would be helpful to assess standard error of design to estimate the precision of the proposed design. The standard error of design is the root mean square error of the design space. Fig. 3 depicts two-dimensional contours and three-dimensional surface of the standard error of D-optimal mixture design being studied. The lighter 117

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Fig. 2. FE-SEM micrographs of (a1, a2) waste foundry ash, (b1, b2) fly ash, and (c1,c2) GPC. The images on the left and the right were taken at 100x and 500x magnification, respectively.

Fig. 3. (a) Two-dimensional contour plot and (b) three-dimensional surface representing the standard error of design at different points in the design space. The standard error is plotted for the changing compositions of A: Fine Aggregates, B: WFS, and C: Fly Ash in a mixture. At a given coordinate in the design space, lighter shading exhibits lower error than the darker shading. The red dots on the contour plot and response surface represent the location of experimental runs in the experimental design space.

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Fig. 4. The experimental versus model predicted compressive strengths for (a) CS 7 and (b) CS 28, based on D-optimal mixture design. The models were developed using training data (circles) and verified using verification data (triangles).

verification data comprises of experiments which were used to verify the models. The perfect prediction line was plotted using the model equations (Eqs. (3) and (4)) after adjusting them for units (Anderson and Whitcomb, 2016). For the training data: in either case (CS 7 and CS 28), the high correlation values (R² ≥0.97) are indicative of least variability between experimental (training) and predicted (model) values of compressive strengths (indicating the good fit of the models). In the case of CS 7; the experimental compressive strengths varied between 5.7–18.9 N/mm2 while the predicted ones varied between 6.2–17.5 N/mm2. On the other hand, for CS 28; the experimental compressive strengths varied between 7.4–21.4 N/mm2 whereas the predicted values varied between 8.0–20.4 N/mm2. For the verification data, the calculated percentage difference between experimental (verification) and predicted (expected or model) values, RMSE, and SEP are presented in Table 3. The highest observed percentage difference was 15.9% in case of CS 7 and 21.6% in case of CS 28. The RMSE and SEP (%) values were calculated to be 1.2 N/mm² and 9.4% for CS 7 and 1.5 N/mm² and 10.4% for CS 28, respectively. The RMSE and SEP values are not extremely good, however, they are reasonable enough to allow the models for the analyses of variance and diagnostics. The experimental CS 7 and CS 28 values are comparable to the existing studies for similar materials. The 7th day compressive strength has been reported to range from 7 N/mm2 (Tennakoon et al., 2014) to 69 N/mm2 (Hardjito et al., 2005); whereas the 28th day compressive strength has been found to vary between 7.3 N/mm2 (van Jaarsveld et al., 2003) to 76 N/mm2 (Lloyd and Rangan, 2010), respectively, depending upon the composition, preparation methods, and geopolymeric reactions (Reddy et al., 2016). Although, our results lie close to the previously reported data, yet more research is required to analyze the influence of variations in sample preparation and impact of geopolymeric reactions under controlled conditions.

Table 4 Analysis of variance (ANOVA) of models for compressive strength of the solidified GPC at 7th (CS 7) and 28th day (CS 28) curing times. Curing time: 7th day Source

Sum of squares

Degrees of freedom

Mean Square

F-value

p-value

Results

Model Linear mixture AB AC BC ABC AB(A-B) AC(A-C) BC(B-C) Pure Error Cor total R2 = 0.97

170.97 19.10 8.80 49.53 48.53 51.23 48.79 48.83 41.82 4.44 175.41 Adj-R2 =

9 2 1 1 1 1 1 1 1 4 13 0.92

19.00 9.55 8.80 49.53 48.53 51.23 48.79 48.83 41.82 1.11

17.13 8.61 7.94 44.67 43.77 46.21 44.00 44.04 37.71

0.0075 0.0355 0.0479 0.0026 0.0027 0.0024 0.0027 0.0027 0.0036

Significant Significant Significant Significant Significant Significant Significant Significant Significant

Adeq precision = 13.5

C.V.% = 7.61

Mean Square

F-value

p-value

Results

23.83 14.33 2.73 65.09 63.89 68.82 73.18 65.23 56.98 0.58

40.91 24.60 4.69 111.74 109.68 118.15 125.62 111.98 97.82

0.0014 0.0057 0.0963 0.0005 0.0005 0.0004 0.0004 0.0005 0.0006

Significant Significant Significant Significant Significant Significant Significant Significant Significant

Curing time: 28th day

3.3. Analysis of variance (ANOVA) and diagnostics ANOVA was performed to investigate the significance of the models and their terms. Table 4 shows the results of ANOVA for CS 7 and CS 28. Significant interacting process-based parameters were determined using the Fisher-variance ratio (F-value) and probability-based value (pvalue). When F-value is greater than the p-value and p-value is lower than 0.05, independent variables are statistically significant at 95% confidence level (Pattanaik and Rayasam, 2018; Pillai et al., 2017). As shown in Table 4, both CS 7 and CS 28 models and all their terms are statistically significant at 95% confidence level except the term AB of CS 28 which is significant at 90% confidence level (Pattanaik and Rayasam, 2018). The F-values (17.13 and 40.91 for CS 7 and CS 28, respectively) of the models are significantly higher than one and thereby indicate that the variances contributed by the models are larger than random errors (Anderson and Whitcomb, 2015). The ratio of the

Source

Sum of squares

Model Linear mixture AB AC BC ABC AB(A-B) AC(A-C) BC(B-C) Pure Error Cor total R2 = 0.99

214.49 28.66 2.73 65.09 63.89 68.82 73.18 65.23 56.98 2.33 216.82 Adj-R2 =

Degrees of freedom 9 2 1 1 1 1 1 1 1 4 13 0.97

Adeq precision = 20.5

C.V.% = 4.74

model’s mean squares to errors’ mean squares is ˜17 times higher in case of CS 7 and ˜41 times higher in case of CS 28. Therefore, both the higher F-values and lower p-values verify the statistical significance of the developed models (Anderson and Whitcomb, 2015; Zafar et al., 2017). Similarly, for the model terms, the lowest F-values for CS 7 and CS 28 models were 7.94 and 4.69, respectively, and their corresponding highest p-values were 0.048 and 0.096, respectively. In statistical modeling, the coefficient of determination i.e. R2 typically represents the fit of the model. However, adjusted R2 (Adj-R2) is recommended to analyze the model fit quality and its significance (Anderson and Whitcomb, 2016). Our results exhibit high Adj-R2 values for both CS 7 119

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Fig. 5. Diagnostics of CS 7 model by (a) normal plot of residuals, (b) cook’s distance, (c) leverage plot, and (d) residuals vs predicted plot for 7th day compressive strength. Diagnostics of CS 28 model by (e) normal plot of residuals, (f) cook’s distance, (g) leverage plot, and (h) residuals vs predicted plot for 28th day compressive strength. The diagnostics suggest the normal distribution of data and the absence of outliers.

(Adj-R2 =0.92) and CS 28 (Adj-R2 =0.97). Thereby, verifying the good fit of models to the experimental data. The precision adequacy (ratio between signal and noise) values for CS 7 and CS 28 were found to be 13.5 and 20.5, respectively, which are reasonably above the acceptable value of 4 for the said parameter (Anderson and Whitcomb, 2016; Sengupta, 2014). The coefficient of variation (CV%) was analyzed for the validation of precision treatment. It helps in identifying the level of data spread from the mean. The CV% values ≤ 10% correspond to higher experiment reliability (Li et al., 2009). The CV% value for CS 7 and CS 28 were found to be 7.61% and 4.74%, respectively. Consequently, ANOVA authenticates the significance of CS 7 and CS 28 models and all their terms and recommends for the diagnostics of the models. Diagnostics were performed to test whether the errors were independent and identically distributed. Diagnostics of the models were performed by graphically analyzing the model through plotting: normal plot of residuals, cook’s distance, leverage plot, and residuals vs predicted response plots (Anderson and Whitcomb, 2016). Fig. 5a shows the normal plot of residuals for the CS 7 model. It can be seen that the internally studentized residuals generally follow the normal distribution and, therefore, the model is adequate and cannot be significantly improved by transforming the response (Jeirani et al., 2012; Varanda et al., 2017). Internally studentized residuals measure the difference in the number of standard deviations between actual and predicted values and can be obtained by estimating the standard deviations of that residual (Anderson and Whitcomb, 2016). A similar distribution of residuals was observed for CS 28 model (Fig. 5e). The influence of an individual experimental run on the overall model can be diagnosed by plotting Cook’s distance (Fig. 5b and f) (Anderson and Whitcomb, 2016). It estimates the change in the parameters of the model upon omitting a specific experimental run and therefore helps in spotting the outliers (Varanda et al., 2017). Fig. 5b and f suggest uniform data and the absence of outliers since all the data are uniformly distributed. Analysis of leverage (Fig. 5c and g) and residuals vs predicted (Fig. 5d and h) plots endorse the findings of normal probability plots of residuals and Cook’s distance plots by negating the presence of any trend.

The absence of trend (i.e. the uniform distribution of data) in leverage and residuals vs predicted plots imply the uniform impact of experimental runs on modeled responses (Varanda et al., 2017). Therefore, it can be safely assumed that the model is representative of experimental data and can be navigated throughout the design space. The results from diagnostic plots endorse the findings of the standard error of design (Fig. 3), experimental verification (Table 3 and Fig. 4), and ANOVA (Table 4). The combined impacts of mixture components (factors) on compressive strengths (responses) were assessed by generating the response surface plots using model equations.

3.4. Effects of mixture components on compressive strength of GPC The impacts of mixture components on CS 7 and CS 28 can be estimated by analyzing response surfaces (Fig. 6) generated from empirically developed (Eqs. (3) and (4)), experimentally verified (Table 3 and Fig. 4), ANOVA analyzed (Table 4), and statistically validated (Figs. 3–5) empirical models. Fig. 6a shows the contour plot and Fig. 6b represents the corresponding response surface depicting the impacts of mixture components on the 7th day compressive strength of GPC. The locations of experimental runs are represented by red dots on the response surfaces. The figure shows two regions, red (≥30 N/mm2) and orange (≥25 N/mm2) contours in Fig. 6a and elevated surface in Fig. 6b, where the model predicts very high compressive strengths. The red region majorly lies around the highest proportion of fine aggregates (55 wt.%) along with its minor portion residing close to the highest proportion of foundry sand (40 wt.%). Also, maximizing the proportion of fly ash (30%) appears more suitable with a high proportion of fine aggregates i.e. a compressive strength of ˜20 N/mm2 (yellow region) can be achieved by limiting the foundry sand contribution to 34 wt.% and increasing the proportion of the fine aggregates to 36 wt.%. However, the highest CS 7 (˜30 N/mm2) can only be achieved by maximizing the proportion of fine aggregates (54 wt.%) and limiting the foundry sand and fly ash contribution to ˜21 wt.% and ˜25 wt.%, respectively. Fig. 6c and d, representing the response surface analysis of the CS 28, follow similar (to CS 7) trends. Although, the two 120

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Fig. 6. (a) Two-dimensional contour and it's corresponding (b) three-dimensional response surface generated from a cubic model developed for CS 7. (c) Twodimensional contour and its corresponding (d) three-dimensional response surface generated from a cubic model developed for CS 28. The red dots on the plots represent the location of experimental runs performed to develop the models. The red regions are indicative of the highest compressive strength.

(compressive strengths) differ in magnitude, yet, the highest compressive strength can be achieved by maximizing the relative proportion of fine aggregates (˜40 N/mm2) followed by foundry sand (˜35 N/mm2) and fly ash (˜25 N/mm2). In essence, the response surfaces presented in Fig. 6 can be helpful in identifying the required proportion of fine aggregates, foundry sand, and fly ash for the desired compressive strengths. These response surfaces were further investigated to identify the optimum mixture composition to achieve reasonably high compressive strength through the minimal consumption of fine aggregates and maximum utilization of WFS and fly ash.

Table 5 Optimization criteria for GPC synthesis. Variables

Units

Lower limit

Upper limit

Optimization target

Fine aggregate WFS Fly ash 7th day compressive strength 28th day compressive strength

wt. % wt. % wt. % N/mm2

30 15 20 5.7

55 40 30 18.9

Minimize Maximize Maximize Maximize

N/mm2

7.4

21.4

Maximize

3.5. Optimization whereas, the two responses were targeted to meet or exceed M20 grade concrete as shown in Table 5. The optimization was performed by employing the desirability function which combines all the optimization targets into one (desirability) function (Myers et al., 2016Myers and Montgomery, Douglas C. Anderson-Cook, 2016). The mathematical expression used to describe desirability can be written as Eq. (5) (Anderson and Whitcomb, 2016; Gunst et al., 1996):

The primary objective of this study was to optimize the utilization of WFS and fly ash for GPC synthesis, therefore, optimization criteria were developed (Table 5). The formulated optimization targets were to: (i) minimize the proportion of fine aggregates, (ii) maximize the proportion of WFS and fly ash, and (iii) to maximize the CS 7 and CS 28. The equal weights were assigned to all the variables (three factors and two responses). The entire studied ranges of the three factors were scanned, 121

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Fig. 7. Numerical optimization for minimizing the proportion of fine aggregates and maximizing the proportion of WFS and fly ash to synthesize GPC. (a) Desirability plot shows the flag point where the optimized design mixture composition exists. The desirability of 0.97 (Table 5) can be achieved by mixing 32 wt. % fine aggregates, 38 wt. %WFS, and 30 wt. % fly ash. The optimized mixture composition results in (b) 18.9 N/mm² for CS 7 and (c) 22.3 N/mm² for CS 28.

n

1 n

⎛ ⎞ D = ⎜∏ d i ⎟ = (d1 × d2 × d3…………×dn ) i = 1 ⎝ ⎠

ash (30%). Our findings demonstrate the robustness and usefulness of D-optimal mixture design to model, predict, and optimize the GPC synthesis process using WFS and fly ash. The findings can be helpful for the efficient use of WFS and fly ash as an eco-friendly partial replacement of fine aggregates. This might bring significant value-addition to the construction industry through the reuse of by-products and reduction of natural virgin mineral resource consumption. This approach can be adapted to evaluate other waste materials for effective utilization. The benefits of incorporating WFS and fly ash into large scale concrete applications include virgin mineral resource conservation, waste utilization, mining-based CO2 emission reduction, and reduced impacts associated with waste disposal.

1 n

(5)

where “D” represents desirability function, “di” (ranges from d1 to dn) denotes the desirability of each individual response, and “n” is the number of responses which are being optimized. The overall desirability function becomes zero if any of the responses reaches zero because the equation involves multiplication of responses. Desirability function maximizes as the factors and responses move closer to the targets (Table 5) and becomes zero if any of the factors or responses fall outside their desirability range. The optimization results are presented in Fig. 7. Fig. 7a shows the flag at the “sweet spot”, in a desirability plot, where all the factors and responses were optimized and the highest desirability i.e. ˜0.97 (against the set criteria in Table 5) was achieved. It is worth noting that the optimized mixture composition requires only 32 wt. % fine aggregates and utilizes 38 wt.% of WFS and 30 wt.% of fly ash. The optimized composition yields 18.9 N/mm2 CS 7 (Fig. 7b) and 22.3 N/mm2 CS 28 (Fig. 7c).

Acknowledgment This research work was financially supported by 2018 research fund of the University of Ulsan (Grant number: 2018-0337). References Abdollahnejad, Z., Pacheco-Torgal, F., de Aguiar, J.B., 2015. Development of foam onepart geopolymers with enhanced thermal insulation performance and low carbon dioxide emissions. Adv. Mater. Res. 1129, 565–572. https://doi.org/10.4028/www. scientific.net/AMR.1129.565. Anderson, M.J., Whitcomb, P.J., 2016. RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, 2nd ed. Productivity Press, New York. https://doi.org/10.1201/9781315382326. Anderson, M.J., Whitcomb, P.J., 2015. DOE Simplified: Practical Tools for Effective Experimentation, 3rd ed. Productivity Press, New York. https://doi.org/10.1201/ b18479. Anderson, M.J., Whitcomb, P.J., Bezener, M.A., 2018. Formulation Simplified: Finding the Sweet Spot Through Design and Analysis of Experiments With Mixtures, 1st ed. Productivity Press, New York. https://doi.org/10.4324/9781315165578. Arroyo-López, F.N., Bautista-Gallego, J., Chiesa, A., Durán-Quintana, M.C., GarridoFernández, A., 2009. Use of a D-optimal mixture design to estimate the effects of diverse chloride salts on the growth parameters of Lactobacillus pentosus. Food Microbiol. 26, 396–403. https://doi.org/10.1016/J.FM.2009.01.009. Baek, J.W., Choi, A.E.S., Park, H.S., 2017. Solidification/stabilization of ASR fly ash using Thiomer material: optimization of compressive strength and heavy metals leaching. Waste Manag. https://doi.org/10.1016/j.wasman.2017.09.010. Bakharev, T., 2006. Thermal behaviour of geopolymers prepared using class F fly ash and elevated temperature curing. Cem. Concr. Res. 36, 1134–1147. https://doi.org/10. 1016/J.CEMCONRES.2006.03.022. Basar, H.M., Aksoy, N.D., 2012. The effect of waste foundry sand (WFS) as partial replacement of sand on the mechanical, leaching and micro-structural characteristics of ready-mixed concrete. Constr. Build. Mater. 35, 508–515. https://doi.org/10.1016/J. CONBUILDMAT.2012.04.078. Bernal, S.A., Provis, J.L., 2014. Durability of alkali-activated materials: progress and perspectives. J. Am. Ceram. Soc. 97, 997–1008. https://doi.org/10.1111/jace.12831. Bhardwaj, B., Kumar, P., 2017. Waste foundry sand in concrete: a review. Constr. Build.

4. Conclusions Our research focused on GPC synthesis with partial replacement of fine aggregates with WFS and fly ash, both of which are usually considered as process by-products. The final serviceability and performance of the solidified GPC were based on CS 7 and CS 28. Using the Doptimal mixture design, different mixing proportions of fine aggregate, WFS, and fly ash were formulated. Highest experimental compressive strength for CS 7 (18.9 N/mm2) and CS 28 (21.4 N/mm2) was observed for a design mix containing 55.0% fine aggregates, 15–23.3% WFS and 21.7–30 % fly ash for a standard unit sample weight of 2400 kg/m3. The experimental data were used to develop empirical models to understand the impacts of mixture compositions on CS 7 and CS 28. The models were statistically tested (for the standard error of design), experimentally verified (by calculating % error, RMSE, and % SEP of verification data obtained from an independent set of experiments), and statistically validated (through ANOVA and diagnostics) before generating response surfaces. The response surfaces generated from the models predicted that 20–25 N/mm2 compressive strength (for CS 7 and CS 28, respectively) could be achieved by varying the mixture composition in maximum waste consumption regions. Finally, the optimized recipe was identified to achieve 18.9 N/mm2 CS 7 and 22.3 N/ mm2 CS 28 by replacing 68% fine aggregates with WFS (38%) and fly 122

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