Sustainable Perspective of Low-Lime Stabilized Fly Ashes for Geotechnical Applications: PROMETHEE-Based Optimization Approach

: In the present scenario of global green environmental and sustainable management, the disposal of large volumes of coal-based ashes (ﬂy ashes) generate signiﬁcant environmental stress. The aim is to exploit these ﬂy ashes for bulk civil engineering applications to solve societal-environmental issues employing sustainable measures. In this study, the addition of lime and / or gypsum in improving the geotechnical properties (hydraulic conductivity, compressibility, unconﬁned compression strength, lime leachability, and California bearing ratio) of ﬂy ashes was investigated. To assist the practicing engineers in selecting the right mix of lime and / or gypsum for a given amount of ﬂy ash for a speciﬁc application, a multi-criteria approach was adopted. The possible alternatives investigated included untreated ﬂy ash, ﬂy ash treated with lime (1%, 2.5%, 5%, or 10%), and a variation in gypsum dosage (1% or 2.5%) in the presence of lime. Sensitivity analysis was performed to recognize and resolve the conﬂicting advantages and disadvantages when mixing lime and gypsum. The study revealed that to derive the potential beneﬁts of ﬂy ash, it is essential to combine the lime dosage with gypsum for pavement and liner applications where bulk quantities of ﬂy ash are employed.


Introduction
Current sustainable energy policies reflect thermal power generation as a major mode of power generation that facilitates industrial development worldwide. A total of 7727.3 Mt of coal was produced worldwide in 2017 [1]. The production of large quantities of sustainable fly ash [2] necessitates adequate disposal facilities, owing to its negative environmental impact [3]. Additionally, the higher disposal costs associated with fly ashes necessitate its recycling for sustainable development. However, the bulk utilization of fly ashes in various civil engineering applications assists in solving sustainable societal and environmental needs, such as liner material for landfills, sub-base material for pavements, backfill material for embankments and retaining walls, substitute material for sand, aggregate, and cement, and other domestic purposes [4][5][6][7][8][9][10][11][12][13][14]. The various engineering properties that facilitate the utilization of fly ashes, especially in geotechnical applications, are elaborated on in Table 1. Table 1. Summary of the geotechnical properties of fly ashes and fly-ash-amended composites.

Type of Fly Ash and Properties Studied Potential Sustainable Applications Reference
Fly ash compacted under controlled test conditions indicated an increase in Φ from 30 • to 40 • (from DST and Triaxial shear test). Average Cc was 0.2. Satisfied embankment-fill material criteria. DiGioia and Nuzzo [15] Class C fly ash and sand mixtures; 10% bentonite; HC increased by a factor of 1000. Satisfied liner material criteria for hazardous waste. Edil et al. [16] Coal fly ash with lime dust and bentonite (7:2:1 ratio) decreased HC by a factor of 1000, compared to fly ash alone. Migration of heavy metals (Pb, Zn, and Fe) was estimated to be less than 0.10 m (under MSW landfill conditions) over 15 years of service.
Satisfied landfill-barrier material criteria Nhan et al. [17] Class C fly ash (20% by weight) with Class F fly ash reduced HC value by a factor of 100. Durability studies revealed HC value increased by a factor between 2 and 3 for wetting and drying cycles.
Satisfied the requirements of liner for waste-containment facilities. Palmer et al. [6] The average HC values of Class F fly ash with 30% bentonite reduced by a factor of 10,000. Significant increase in Φ (33 • to 42 • ) and C (36 to 54 kN/m 2 ) from a Triaxial shear test (CU) was observed.
Satisfied the requirements of liner and cover materials for waste-disposal sites. Mollamahmutoglu and Yilmaz [18] UCS increased by a factor of 2.2 for alkali-activated Class F fly ash. Percentage weight loss decreased by a factor of 5, compared to PCC subjected to acid immersion (at specified curing periods).
Satisfied the requirement of PCC used for construction purpose. Rostami and Brendley [19] The HC value of Class F and bottom ashes decreased by a factor of 10 with an increase in Class F fly ash content. Satisfied the requirements of highway-embankment material. Kim et al. [8] The Cc of sedimented class F fly ash deposits was relatively higher (by 3%) when compared to compacted conditions.
Satisfied the requirements of sub-base material and lightweight infrastructure. Sekhar et al. [10] With an increase in compaction energy, HC values of Class F fly ash decreased by a factor of 1000. At each compaction energy, a higher CBR value was observed for a water content equal to or slightly less than OMC.
Satisfied the requirements of barrier material for landfill applications. Zabielska-Adamska [20] The HC values of Class F fly ashes reduced by a factor of 1000 and the Cc value was reduced by a factor between 6 and 10 due to the addition of lime (at 10%) and gypsum (at 2.5%).
Satisfied the liner material requirements. Moghal and Sivapullaiah [21]; Moghal and Sivapullaiah [22] UCS values and the CBR of Class F fly ashes increased by a factor of 20 and 7.5, respectively, due to the addition of lime (at 2.5%) and gypsum (at 2.5%).
Satisfied the requirements of sub-base material in road construction. Sivapullaiah and Moghal [23] Note: In above As most fly ashes produced from harder, older, bituminous, and anthracite coals have very low lime content (Class F type), they require cementing agent(s) in the form of lime, gypsum, or cement to enhance their applicability for various civil engineering applications, as discussed in the above Table 1. The addition of lime will supplement a basic cementing agent to produce pozzolanic compounds over time by dissolving the silica-rich, glassy phases of fly ashes [23][24][25]. Because lime-fly ash reactions are time-dependent, the addition of gypsum is considered in order to increase the rate at which pozzolanic compounds are formed. When gypsum is added to lime-treated fly ashes, it provides both advantages and disadvantages; it retards the setting time and accelerates the strength [23,26].
The present study focused on the sustainable perspective of lime and/or gypsum addition on the enhancement of the geotechnical properties (hydraulic conductivity (HC), unconfined compression strength (UCS), compressibility characteristics (Cc), lime leachability (LL), and California bearing ratio (CBR)) of two different types of fly ashes. Both lime and gypsum, at varying dosages, were added to enhance the cementation effect. To assist practicing engineers in selecting the appropriate mix of fly ash, lime, and gypsum contents for specific civil engineering applications, a multi-criteria decision-making (MCDM) approach was adopted. An MCDM approach has been successfully applied in civil engineering applications pertaining to hydraulics [27][28][29], energy [30], solid waste [31,32], transportation [33], green building materials [34], and sustainable development [35].
In the current study, an MCDM approach [36][37][38] was employed to evaluate the different geotechnical properties of fly ashes, stabilized with lime and gypsum at different dosages, for targeted bulk applications. In the present approach, concepts pertaining to preference flow, sensitivity analyses, and graphical interactive analyses were formed to assist practicing engineers in ranking treatment strategies (i.e., determining the correct dosages of lime and gypsum) to establish the superiority of one treatment strategy over another.

Methodology
The adopted methodology was based on the variation of three factors: type of fly ash, lime dosage, and gypsum dosage ( Table 2). The combined influence of each factor, at different levels of interaction, was investigated with regard to the response of the resultant geotechnical properties for specific liner and pavement applications.

Materials Used
This study used two low-lime fly ashes, named A and B (Table 3), sourced from thermal power plants in Neyvelli and Muddanur, which are towns in the states of Tamil Nadu and Andhra Pradesh in India, respectively. Analytical-reagent (AR) grade gypsum (CaSO 4 2H 2 O) and AR grade hydrated lime (Ca(OH) 2 ), supplied by Merck limited, India, were used in the present study. The specific surface area values (SSA) of fly ashes A and B were found to be 9.6 and 8.2 m 2 /gm, respectively [25]. Both fly ashes exhibited non-plastic behavior, with fly ash B having greater fines content compared to fly ash A. Mullite and Quartz phases were predominant in both fly ashes.  Table 4 shows the UCS values for the cured fly ashes A and B. CBR tests were conducted, in accordance with IS 2720 Part 16 [40] and ASTM D1883 [41], on samples cured for 14 days under controlled humidity conditions, and the results are presented in Table 4. Hydraulic conductivity tests were carried out, in accordance with ASTM D5856 [42], on samples cured for 28 days. Compressibility tests were carried out on standard, one-dimensional odometer consolidation tests, as per IS 2720 Part 15 [43] and ASTM D2435 [44]. The compression index values, corresponding to the loading increment from 25 to 50 kPa, are reported in Table 4. The experimental test setup and specimen-testing details are provided in Figure 1. An LL-testing procedure, developed by Moghal and Sivapullaiah [22], was employed in this study. The LL values of cured fly ashes, under 7 days of steady flow conditions, are reported in Table 4. A minimum of three tests were carried out (in triplicates) for each of the studied parameters (hydraulic conductivity; unconfined compressive strength test; California bearing ratio; lime leachability and one-dimensional oedometer fixed-ring consolidation test) as per ASTM standards, and the average values are reported in Table 4.

Experimental Results
The following sections address the fundamental governing mechanism responsible for the enhancement of the targeted properties and the PROMETHEE strategy adopted to choose the ideal mix of fly ash, lime, and gypsum for the selected fly ashes. Pozzolanic reactions of lime (as Ca(OH)2) resulted in the formation of gels, followed by crystallization, namely of calcium silicates, aluminates,

Experimental Results
The following sections address the fundamental governing mechanism responsible for the enhancement of the targeted properties and the PROMETHEE strategy adopted to choose the ideal mix of fly ash, lime, and gypsum for the selected fly ashes. Pozzolanic reactions of lime (as Ca(OH) 2 ) resulted in the formation of gels, followed by crystallization, namely of calcium silicates, aluminates, and alumina silicates. Precipitation and even distribution of hydration products (Ca(OH) 2 , C-S-H, etc.) on the surfaces of fly ash contributed to the additional increase in the strength of the stabilized mix. The rate of formation of stabilized compounds, which increases with the curing period, resulted in the reduction of the LL ratio, defined as the ratio of lime leached to the total lime added. It has been demonstrated that the LL decreases with an increase in the lime content. Sulphate ions from gypsum reacted with the alumina phase of fly ash to produce (x CaO. y Al 2 O 3 . z CaSO 4 . w H 2 O), which enhanced the pozzolanic activity of lime-stabilized fly ashes with higher strength (refer to Table 4). Furthermore, the formation of C-S-H and C-A-S-H gels reduced the LL of fly ashes and increased the unconfined compressive strength and California bearing ratio behavior, as seen in Table 4. Additionally, the resultant denser matrix significantly reduced the hydraulic conductivity values and compression index values (refer to Table 4).

MCDM Model: Based on PROMETHEE and GAIA
For the application of the proposed model, the first step was to develop alternative strategies to enhance various geotechnical properties of fly ash. Initially, the untreated fly ashes were adopted for the geotechnical application and were subsequently treated with lime (1%, 2.5%, 5%, and 10%) and a variation in gypsum dosage (1% and 2.5%) in the presence of lime. Thus, a total of 26 (2 × 1 + 2 × 4 + 2 × 4 × 2) alternative strategies were adopted in the present study. For each alternative strategy, three samples were used, and for each sample, five performance measures were obtained. Thus, 390 performance results were obtained and are reported in Table 4. From the results, it is evident that no individual experiment was superior in terms of all five performance measures. The adopted MCDM model was based on PROMETHEE and geometrical analysis for interactive aid (GAIA), proposed by Brans and Mareschal [45]. PROMETHEE was adopted for the partial and complete ranking of the selected alternative treatment strategies; GAIA was used for the sensitivity and comparative analysis of experimental strategies.

PROMETHEE Partial Ranking
T a and T b denote the treatment strategies used to enhance various geotechnical properties for the given fly ash. The effectiveness of the treatment strategies was measured based on multiple performance measures, such as j performance measures. R aj and R bj are the performance measures for treatment strategies T a and T b , respectively. In order to set a partial preference, a generalized preference function P abj was defined for performance measure j, when alternative treatment strategies T a and T b were compared; P abj fluctuates between zero and one (refer to Figure 2). The value of P abj can be interpreted as follows: • P abj ≈ one, indicates acceptable preference for strategy T a over strategy T b . • P abj ≈ zero, indicates a weak preference for strategy T a over strategy T b . • P abj = zero, indicates no preference for strategy T a over strategy T b for a given j performance measure. • P abj = one, indicates a higher preference for strategy T a over T b .
Preference functions (P abj ) were adopted [45] based on the response measures (R aj and R bj ) and the permissible boundary to derive a full incompatibility (θ) and a full preference (Ø). The permissible boundary values θ and Ø were set by the decision-maker.  Contrary to Pab, Pba is a preference function to estimate the dominance of treatment strategy Tb over Ta, and n is the number of treatment strategies. For "n" given treatment strategies, each is compared to n-1 other treatment strategies. Thus, the objective in partial ranking is to maximize outranking flows (F+) and to minimize outranking flows (F−); these outranking flows express how Ta outranks the other n-1 treatment strategies. By determining positive (F+a and F+b) and negative (F−a and F−b) outranking flows for a pair of treatment strategies Ta and Tb, the dominance relationship can be inferred as shown below: • If F+a > F+b, the outranking relationship is D+ab (i.e., treatment strategy Ta is dominating Tb).

•
If F−a < F−b, the outranking relationship is D−ab (i.e., treatment strategy Ta is not dominating Tb).

•
If F+a = F+b, the outranking relationship is ED+ab (i.e., both treatment strategies Ta and Tb equally dominate (ED) the other "n − 2" treatment strategies).

•
If F-a = F-b, the outranking relationship is ED-ab (i.e., both treatment strategies Ta and Tb are equally dominated (ED) by the other "n − 2" treatment strategies).
Based on the above conditions, three PROMETHEE-based partial rankings were computed as follows to establish the preference relationship between Ta and Tb: • If (D+ab and D−ab)/(D+ab and ED−ab)/(ED+ab and D−ab) are true, it indicates that treatment strategy Ta has a higher preference over Tb.

•
If ED+ab and ED−ab are true, it indicates that treatment strategy Ta is no different to Tb. • If (D+ab and D−ba)/(D+ba and D−ab) are true, it indicates that treatment strategy Ta is in contrast to Tb. Specifically, on a set of geotechnical performance measures, Ta exhibits the best performance over Tb, which exhibits a low response and vice versa.
In order to determine the best treatment strategy, PROMETHEE partial ranking was extended to PROMETHEE complete ranking. Subsequently, considering all "k" performance measures (j = 1 to k), the preference function value P ab for a pair of treatment strategies T a and T b was computed using Equation (1). In Equation (1), W j is the weight assigned to each performance measure j; P abj is a preference function allocated to a pair of treatment strategies T a and T b for each j. P ab fluctuates between zero and one.
However, in practice, more than two treatment strategies exist to enhance various geotechnical properties for the given coal-based fly ash. In this study, 26 treatment strategies were analyzed to find the superiority of a treatment strategy over the others; this is set by estimating two outranking flows (F + and F -), expressed in Equations (2) and (3). Each outranking flow does not result in the same rank for each treatment strategy. Thus, their (F + and F − ) intersection is induced to estimate partial ranking.
Contrary to P ab , P ba is a preference function to estimate the dominance of treatment strategy T b over T a , and n is the number of treatment strategies. For "n" given treatment strategies, each is compared to n-1 other treatment strategies. Thus, the objective in partial ranking is to maximize outranking flows (F + ) and to minimize outranking flows (F − ); these outranking flows express how T a outranks the other n-1 treatment strategies. By determining positive (F +a and F +b ) and negative (F −a and F −b ) outranking flows for a pair of treatment strategies T a and T b , the dominance relationship can be inferred as shown below: • If F +a > F +b , the outranking relationship is D +ab (i.e., treatment strategy T a is dominating T b ).

•
If F −a < F −b , the outranking relationship is D −ab (i.e., treatment strategy T a is not dominating T b ).

•
If F +a = F +b , the outranking relationship is ED +ab (i.e., both treatment strategies T a and T b equally dominate (ED) the other "n − 2" treatment strategies).

•
If F -a = F -b , the outranking relationship is ED -ab (i.e., both treatment strategies T a and T b are equally dominated (ED) by the other "n − 2" treatment strategies).
Based on the above conditions, three PROMETHEE-based partial rankings were computed as follows to establish the preference relationship between T a and T b : • If (D +ab and D −ab )/(D +ab and ED −ab )/(ED +ab and D −ab ) are true, it indicates that treatment strategy T a has a higher preference over T b .

•
If ED +ab and ED −ab are true, it indicates that treatment strategy T a is no different to T b . • If (D +ab and D −ba )/(D +ba and D −ab ) are true, it indicates that treatment strategy T a is in contrast to T b . Specifically, on a set of geotechnical performance measures, T a exhibits the best performance over T b , which exhibits a low response and vice versa.
In order to determine the best treatment strategy, PROMETHEE partial ranking was extended to PROMETHEE complete ranking.

PROMETHEE Complete Ranking
For complete ranking, outranking flow F a is set as the balance between two outranking flows F + and F -, in reference to Equations (2) and (3); F a is computed using Equation (4). Using PROMETHEE complete ranking, all treatment strategies are comparable.
Based on the outranking flows F a and F b (in reference to Equation (4)), PROMETHEE complete ranking is established between T a and T b as follows: • If F a > F b is true, it indicates that treatment strategy T a is preferred over treatment strategy T b .

•
If F a = F b is true, there is no difference between treatment strategies T a and T b . • If F a < F b is true, it indicates that there is no preference of treatment strategy T a over treatment strategy T b .

Application of PROMETHEE
From above Table 4, it is evident that no treatment strategy exhibits superiority in terms of all five performance measures. Using PROMETHEE, the outranking flows (F + , F − , and F) were obtained for each treatment strategy, for two different types of applications (i.e., liner and pavement applications), as presented in the following Table 5. Using F + , F − , and F, the treatment strategies ranking and superiority of each treatment strategy are presented in Figures 3-6.       In Figure 3, treatment AL2G2 dominates the other treatments and corresponds to fly ash type A treated with lime (2.5%) and gypsum (2.5%) (Tables 2 and 4). It also reveals that pure fly ash type A underperformed compared to other possible treatments. Similarly, both fly ashes with lime treatment alone underperformed compared to alternative treatments. Generally, the outranking scores (F+ and F−) induce two different complete rankings. In order to circumvent this scenario, a complete ranking based on net flow "F" was obtained, as shown in Figure 4. The top half corresponds to F+ and the bottom half to F− scores for each treatment.
Similarly, in the above Figure 4, it is evident that AL2G2 and AL3G2 (for example, as calculation case for AL3G2 refer in the Appendix A, to illustrate how to obtain the value of "F") superseded the other treatment strategies, while A and AL2 were the least preferred. Simultaneously, using F+, F−, and F, a preference network is drawn (refer to Figure 5) where the treatment strategy and its choice over other treatment strategies are denoted by the arrow →. Figure 5 also shows that treatment strategy AL2G2 was preferred over other treatment strategies, with A and AL2, once again, being least-preferred. It is interesting to note that treatment strategy B was preferred over A, AL1, AL2, and AL3. This is due to the fundamental differences in chemical and mineralogical compositions between the two fly ashes (refer to Table 3). The visual PROMETHEE and GAIA plane represents the conflicts between response measures and highlights the group of treatment strategies of remarkable performance (refer to Figure 6).
The GAIA plane is considered to be a geometrical interactive tool, used to assist decision-makers with sensitivity analyses. Treatment strategies are represented by blue squares in Figure 6, and treatment strategies with no difference appear close, while conflicting strategies are placed in different quadrants. The various geotechnical properties of fly ash measures, showing equal preference, lean in the same direction in the GAIA plane while conflicting response measures lean in the opposite direction. From Figure 6, it is clear that the fly ash without any additives or with only a lime dosage treatment are scored opposite to the fly ash with both a lime and gypsum treatment. Fly ash type A, treated with a lime dosage of 10% and gypsum dosage of 1%, scored better for response measures UCS and CBR against fly ash type B, which was also treated with a lime dosage of 10% but a gypsum dosage of 2.5%. Similarly, for the lime-leachability response measure, both fly ashes scored In Figure 3, the left-hand column corresponds to the F + score and the right-hand column to the F − score for each treatment strategy. These scores are oriented such that the best are projected upwards. For each treatment strategy, a representative line is drawn from its F + to the corresponding F − score. For any given two treatment strategies, if the representative lines are parallel, the treatment strategy representing the top line is preferred. However, if the two lines intersect, the corresponding treatment strategies are incomparable.
In Figure 3, treatment AL2G2 dominates the other treatments and corresponds to fly ash type A treated with lime (2.5%) and gypsum (2.5%) (Tables 2 and 4). It also reveals that pure fly ash type A underperformed compared to other possible treatments. Similarly, both fly ashes with lime treatment alone underperformed compared to alternative treatments. Generally, the outranking scores (F + and F − ) induce two different complete rankings. In order to circumvent this scenario, a complete ranking based on net flow "F" was obtained, as shown in Figure 4. The top half corresponds to F + and the bottom half to F − scores for each treatment.
Similarly, in the above Figure 4, it is evident that AL2G2 and AL3G2 (for example, as calculation case for AL3G2 refer in the Appendix A, to illustrate how to obtain the value of "F") superseded the other treatment strategies, while A and AL2 were the least preferred. Simultaneously, using F + , F − , and F, a preference network is drawn (refer to Figure 5) where the treatment strategy and its choice over other treatment strategies are denoted by the arrow →. Figure 5 also shows that treatment strategy AL2G2 was preferred over other treatment strategies, with A and AL2, once again, being least-preferred. It is interesting to note that treatment strategy B was preferred over A, AL1, AL2, and AL3. This is due to the fundamental differences in chemical and mineralogical compositions between the two fly ashes (refer to Table 3). The visual PROMETHEE and GAIA plane represents the conflicts between response measures and highlights the group of treatment strategies of remarkable performance (refer to Figure 6).
The GAIA plane is considered to be a geometrical interactive tool, used to assist decision-makers with sensitivity analyses. Treatment strategies are represented by blue squares in Figure 6, and treatment strategies with no difference appear close, while conflicting strategies are placed in different quadrants. The various geotechnical properties of fly ash measures, showing equal preference, lean in the same direction in the GAIA plane while conflicting response measures lean in the opposite direction. From Figure 6, it is clear that the fly ash without any additives or with only a lime dosage treatment are scored opposite to the fly ash with both a lime and gypsum treatment. Fly ash type A, treated with a lime dosage of 10% and gypsum dosage of 1%, scored better for response measures UCS and CBR against fly ash type B, which was also treated with a lime dosage of 10% but a gypsum dosage of 2.5%. Similarly, for the lime-leachability response measure, both fly ashes scored better when treated with a 1% lime and 2.5% gypsum dosage. However, for the HC and Cc response measures, only fly ash type A with a 1% lime and gypsum dosage scored better.
Sensitivity analysis was adopted to assign weights to each response measure. To perform sensitivity analyses on the experimental data (refer to Table 4), the Visual PROMETHEE-GAIA walking weights tool was used.

Sensitivity Analysis Using Walking Weights
Sensitivity analysis using walking weights was performed by assigning a set of weights to each response measure, as shown in Table 6. Set 1 represents an equal weight allocation for all response measures (Figure 7). Because fly ashes have the most potential to be used in liners and pavement material applications, a sensitivity analysis was carried out for these two specific applications by assigning due weights. The rationale behind weight allocation is application-specific, as seen in Table 6. For a liner material, the driving factor is "Hydraulic conductivity," and it was assigned a higher weight compared to the other response measures (i.e., UCS, CBR, Cc and LL). Similarly, for the pavement application, the significant factor is the "California bearing ratio," and it was duly assigned higher weightage over the other response measures. better when treated with a 1% lime and 2.5% gypsum dosage. However, for the HC and Cc response measures, only fly ash type A with a 1% lime and gypsum dosage scored better. Sensitivity analysis was adopted to assign weights to each response measure. To perform sensitivity analyses on the experimental data (refer to Table 4), the Visual PROMETHEE-GAIA walking weights tool was used.

Sensitivity Analysis Using Walking Weights
Sensitivity analysis using walking weights was performed by assigning a set of weights to each response measure, as shown in Table 6. Set 1 represents an equal weight allocation for all response measures (Figure 7). Because fly ashes have the most potential to be used in liners and pavement material applications, a sensitivity analysis was carried out for these two specific applications by assigning due weights. The rationale behind weight allocation is application-specific, as seen in Table 6. For a liner material, the driving factor is "Hydraulic conductivity," and it was assigned a higher weight compared to the other response measures (i.e., UCS, CBR, Cc and LL). Similarly, for the pavement application, the significant factor is the "California bearing ratio," and it was duly assigned higher weightage over the other response measures. Table 6. Criterion weights allocated for sensitivity analysis.

Criterion Weight UCS (%) CBR (%) HC (%) Cc (%) LL (%)
For liner applications  The sensitivity analysis was done relying on the assigned walking weights above (Set 2), using Visual PROMETHEE. The outcome of the sensitivity analysis for liner and pavement applications is presented in the following Figures 8 and 9, respectively. Figure 8a-e represents the analysis corresponding to the ranking of treatment strategies, superiority of each treatment strategy, GAIA plane, and walking weights, respectively, for liner application. Based on the objectives of the response measures, shown in the above Table 6, a maximum of 40% weighting was set to response measure HC and a minimum of 0% to response measure CBR for this set. Hydraulic conductivity was considered a driving prerequisite for liner applications. Therefore, fly ash type A, treated with a 2.5% dosage of lime and 2.5% dosage of gypsum, was preferred over the alternatives (refer to Table 2). With regard to the lime-leachability values, both of the selected fly ashes responded alike to 1% lime content spiked with 2.5% gypsum. At higher lime contents, readily soluble amorphous lime, which The sensitivity analysis was done relying on the assigned walking weights above (Set 2), using Visual PROMETHEE. The outcome of the sensitivity analysis for liner and pavement applications is presented in the following Figures 8 and 9, respectively. Figure 8a-e represents the analysis corresponding to the ranking of treatment strategies, superiority of each treatment strategy, GAIA plane, and walking weights, respectively, for liner application. Based on the objectives of the response measures, shown in the above Table 6, a maximum of 40% weighting was set to response measure HC and a minimum of 0% to response measure CBR for this set. Hydraulic conductivity was considered a driving prerequisite for liner applications. Therefore, fly ash type A, treated with a 2.5% dosage of lime and 2.5% dosage of gypsum, was preferred over the alternatives (refer to Table 2). With regard to the lime-leachability values, both of the selected fly ashes responded alike to 1% lime content spiked with 2.5% gypsum. At higher lime contents, readily soluble amorphous lime, which is in excess to optimum lime requirements, simply leaches out of the fly ash-lime-gypsum matrix [22]. However, mix AL3G2 showed the maximum possible gain in unconfined compressive strength behavior. Similarly, Figure 9a-e represents the analyses corresponding to the ranking of treatment strategies, the superiority of each treatment strategy, graphical interactive plane, and walking weights, respectively, for pavement applications. In the case of pavement application (refer to Table 6), a maximum of 40% weighting and a minimum of 5% weighting was assigned to the response measures CBR and Cc, respectively. Mix AL3G2 satisfied the requirements for these conditions (refer to Table 2). a maximum of 40% weighting and a minimum of 5% weighting was assigned to the response measures CBR and Cc, respectively. Mix AL3G2 satisfied the requirements for these conditions (refer to Table 2).  Table 6).  Table 6).   Table 6).

Conclusions
The present study investigated the effect of the addition of lime and/or gypsum on improving various geotechnical properties (Cc, HC, UCS, CBR, and LL) of Class F fly ashes. The objective was to assist practicing engineers in the selection of the most effective mix of fly ash type, lime, and gypsum dosage to satisfy various geotechnical properties for specific geotechnical applications. The MCDM model, based on PROMETHEE and GAIA, was adopted to select the best treatment strategy. Multiple treatment strategies were analyzed, based on variations in lime and gypsum dosages, and for each of these alternatives, response measures in the form of geotechnical properties were computed. This approach resulted in obtaining the treatment strategies ranking, superiority of each treatment strategy, and geometrical interactive plane. From these outcomes, it is evident that a group of treatment strategies exhibits similar preferences for given response measures. Thus, sensitivity  Table 6).

Conclusions
The present study investigated the effect of the addition of lime and/or gypsum on improving various geotechnical properties (Cc, HC, UCS, CBR, and LL) of Class F fly ashes. The objective was to assist practicing engineers in the selection of the most effective mix of fly ash type, lime, and gypsum dosage to satisfy various geotechnical properties for specific geotechnical applications. The MCDM model, based on PROMETHEE and GAIA, was adopted to select the best treatment strategy. Multiple treatment strategies were analyzed, based on variations in lime and gypsum dosages, and for each of these alternatives, response measures in the form of geotechnical properties were computed. This approach resulted in obtaining the treatment strategies ranking, superiority of each treatment strategy, and geometrical interactive plane. From these outcomes, it is evident that a group of treatment strategies exhibits similar preferences for given response measures. Thus, sensitivity analysis, based on walking weights, was executed to solve conflicts. In this study, untreated fly ash (A or B) and any fly ash type treated only with lime were found to be the least-preferred options. The proposed MCDM model, based on PROMETHEE and GAIA, works well to assist practicing engineers with identifying the fly ash type, with the appropriate mix of lime and gypsum. The following outcomes were established:

•
For liner applications, fly ash type A, treated with a 2.5% dosage of lime and 2.5% dosage of gypsum, is preferred.

•
For pavement applications, fly ash type A, treated with a 5% dosage of lime and a 2.5% dosage of gypsum, is preferred.

•
When equal walking weights were assigned to all response measures, irrespective of the nature of application, fly ash type A, treated with either a 2.5% or 5% dosage of lime and a 2.5% dosage of gypsum, should be the first choice.
In the eventuality of having fly ash B on-site, it should be treated with a 10% lime and 2.5% gypsum dosage for liner applications. Furthermore, treating it with a 2.5% lime and 2.5% gypsum dosage would meet the pavement application requirements.

Conflicts of Interest:
The authors declare no conflict of interest.

Appendix A. Example Calculation for AL3G2
• R aj and R bj are the performance measures for treatment strategies T a and T b , respectively; • W j is the weight assigned to each performance measure j; and • P abj is a preference function allocated to a pair of treatment strategies T a and T b for each j.
Thus, T 11 (AL3G2) and T 1 (A) denote the treatment strategies 11 and 1, respectively (refer Table 4); and there are five performance measures (i.e., j = 5). Thus, preference function value P 11,1 for a pair of treatment strategies T 11 and T 1 is computed using Equation (1).
From Table 4, it is observed that for treatment strategies T 11 and T 1 : V-shape preference function is allocated to the pair of treatment strategies T 11 and T 1 for each j; and W j = 0.2 equal weight assigned to each j.
The value of P 11 1 j can be interpreted as: P 11 1 1 = one; P 11 1 2 = one; P 11 1 3 = zero; P 11 1 4 ≈ one = 0.9905; and P 11 1 5 = one. Thus, preference function value P 11 1 for a pair of treatment strategies T 11 and T 1 is computed and is equal to 0.7981.
Thus, for treatment strategy T 11 , F 1 is equal to (0.5525 − 0.048) = 0.5045 and this value can be seen in Figure 3 assigned to AL3G2.