CHARACTERIZATION OF GYPSEOUS SOIL STABILIZED WITH FLY ASH GEOPOLYMER ANALYZED BY FL-RBF MODEL

http://www.iaeme.com/IJCIET/index.asp 482 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 11, November 2018, pp. 482–495, Article ID: IJCIET_09_11_048
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
©IAEME Publication Scopus Indexed
CHARACTERIZATION OF GYPSEOUS SOIL
STABILIZED WITH FLY ASH GEOPOLYMER
ANALYZED BY FL-RBF MODEL
Seeta Kamalakar Rao
Research Scholar, Department of Civil Engineering
Koneru Lakshmaiah Education
Deemed University
Green fields, Guntur, Vaddeshwaram, Andhra Pradesh
Dr. Sanjeet Kumar
Associate Professor, Department of Civil Engineering
Koneru Lakshmaiah Education, Deemed University
Green fields, Guntur, Vaddeshwaram, Andhra Pradesh
ABSTRACT
The primary target of this work is focused on the performance of gypseous soil and
the impacts of internal sulfate from gypsum. The simulation analysis optimizes to
reduce the collapsibility potential of gypseous soil stabilized with intrinsic sulfate
attack by utilizing hybrid optimization model. In order to assess the compressive
strength, collapsibility potential for the soaked and unsoaked specimen, coefficient of
Permeability, weight loss, and leachout based on flyash and alkali activated solution
with the assistance of Fuzzy logic (FL) and Radial Bias Function (RBF). The result
demonstrated that the proposed hybrid model (FL-RBF) gives the minimum error rate
when compared to individual algorithms.
Key words: gypseous soil, internal sulfate attack, geopolymerized fly ash, gypsum,
FL, and RBF.
Cite this Article: Seeta Kamalakar Rao, Dr. Sanjeet Kumar, Characterization of
Gypseous Soil Stabilized with Fly Ash Geopolymer Analyzed by FL-RBF Model,
International Journal of Civil Engineering and Technology (IJCIET) 9(11), 2018, pp.
482–495.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11
1. INTRODUCTION
A lot of soils can demonstrate risky in geotechnical building, since they extend, crumple,
scatter, experienced by unreasonable settlement to have a particular absence of strength, or are
solvent. Such qualities might be owing to their sythesis, the nature of their pore liquids, their
mineralogy, or their texture [1]. Geopolymer has been gotten from the response between
aluminosilicate material and an alkaline solution to deliver a solid chemical bonding [2]. The

Seeta Kamalakar Rao, Dr. Sanjeet Kumar
critical procedure in this response called geopolymerization. The assurance of good bonding
either solid or awful is relying upon this procedure [3]. Geopolymers, with other alkaline
actuation materials, are gotten from low calcium aluminosilicate materials, for example, fly-
ash that is activated in a shape like gel of three-dimensional system spoke to by the chemical
structure N-A-S-H [4]. Today, the geopolymeric response is another decision to discard
overwhelming metal squanders. The geopolymer pulls in generous consideration on account
of its high compressive strength, low porousness [5]. The utilization of geopolymer materials
as soils stabilizers has been generally examined and aftereffects of such past examinations
show that geo-polymers could be utilized as a viable soil stabilizer [6].
It is notable that geopolymer based materials have an extensive variety of uses in the
structural designing business sector. Tremendous consideration has been set to their
utilization since geopolymers have low porosity, incredible mechanical properties, sturdiness
and thermal strength [7].Since the fly ash is having pozzolanic property. It can be used as an
alternate cementitious material in structural designing applications. The transfer issue of
flyash can be maintained a strategic distance from up to a specific degree by utilizing it for the
development of streets, landing strips, and banks and in fly powder block industry and so
forth [8]. In later past, FA-based geopolymer was effectively utilized by a few specialists for
development of clayey soil [9]. Hence, considerate the building properties of soil are vital to
get strength and monetary perpetual quality. Soil stabilization is the way toward boosting the
reasonableness of the soil for a given development reason [10]. Henceforth, soil change is a
vital component in concentrate the geotechnical properties of gypseous soil under natural
conditions [11]. Fly ash comprises of particles emerging from the burning of pummeled coal
at high temperatures running from around 1400 °C to 1700 °C. Wide-scale coal terminating
for control age started in the 1920s [12] and from that point forward a large number of huge
amounts of fly fiery remains squander have been created far and wide every year [13].
A few analysts have lately explored the utilization of reused squander materials as
substitutes for virgin materials in the common foundation applications, for example, in
asphalts, pathways and dikes [14]. Change of clayey soil by treating with lime, bond, slag and
fly powder are recently set up strategies utilized usually around the world. Lime adjustment is
favored as a stabilizer as a result of its effortlessness and general economy of development
[15]. Trial investigation about on expansive soil by settling it with geopolymer by 6%,
Specific gravity test, Liquid limit; Plastic limit, Compaction test, and Vane shear test were
performed[16]. Primarily, it oﬀers an exit plan for the huge volume of Metropolitan Strong
Waste Cremation ﬂyAsh (MSWIFA) created by industrialized society and furthermore finds a
manageable building material for the developing interest of construction market [17].
2. LITERATURE REVIEW
In 2017 Sara Rios et al. [18] Alkali-Activated Cement (AAC) has been widely examined for
various applications as another option to Portland cement (which has a high carbon
impression) and because of the likelihood of including waste materials such fly ash or slags.
Have proposed the silty sand was balanced out with an AAC blended from low calcium fly
ash and an alkaline solution produced using sodium silicate and sodium hydroxide. Strength
and firmness results demonstrated a huge advancement for 28 curing days yet with a sensible
immediate strength. Durability properties identified with resistance to inundation and WD
cycles were found to agree to existing details for soil– bond, giving legitimacy for its
utilization as soil– concrete substitution.
Potential of Support Vector Machine Regression (SVR) strategy for strength assessment
of Geopolymer balanced out clayey soil has been explored by Ruhul Amin Mozumder et al. in
2017[19]. The database contains 28-day unconfined compressive strength (UCS)

Characterization of Gypseous Soil Stabilized with Fly Ash Geopolymer Analyzed by FL-RBF Model
consequences of soil tests created with various mixes of exploratory parameters. In this
manner, a parametric report with SVR model was led to assess the impact of information
parameters on UCS. Patterns of the outcome acquired from the parametric examination were
observed to be in great concurrence with past research discoveries. At long last, utilizing the
SVR display, an experimental approach for strength expectation of GGBS based geopolymer
settled clayey soil was proposed for down to earth application reason.
In 2017 P. Priyadarshini et al. [20] were outlined utilizing the central composite plan of
response surface strategy. Molarity of NaOH, curing temperature and fly ash content were the
key parameters considered in this investigation. Soil-based geopolymer mortar brought about
lower thickness goes contrasted with traditional geo-polymer of comparative strength
esteems. The test outcomes demonstrated that strength and shrinkage properties of soil
construct geopolymer mortar fundamentally depends with respect to the sort of earth exhibit
in the soil. Geopolymer blend with every particular soil has an ideal mix of NaOH, curing
temperature and folio measurement that causes them accomplish the coveted properties, for
example, higher compressive strength and lower dry thickness, water ingestion, and shrinkage
esteems.
This examination explored (Jean-Baptiste MawuléDassekpo et al in 2017) [21] the
potential utilization of common loess to initiate a geopolymerization response, and
furthermore to recognize the proficient fly ash proportions that are to be joined with loess for
the improvement of another geopolymer material. A blend of two concoction solutions,
sodium hydroxide (NaOH) and sodium silicate (Na2SiO3), and in addition a plasticizer, was
utilized to research the union response and the compressive execution on loess-fly ash based
geopolymer glues. It was discovered that the geopolymer glues got from 90% loess and 10%
fly ash proportions speak to the most noteworthy proportion with compressive strength
estimation of up to 14.54 MPa at 7 days curing period.
This examination concentrated on impacts of inborn sulfate in gypseous soil on its
collapsibility potential when settled with fly ash geopolymer folio by ShaymaaAlsafi et al.
[22]. Consequently, pressure and collapsibility tests were performed on the cover and the
balanced out soil, individually. XRD, TGA, and SEM/EDX tests were likewise led to follow
changes because of geopolymerization and sulfate assault when introduction at various
periods of up to 90 days. The outcomes demonstrated the arrangement of geopolymer gel (A-
S-H) with higher strength and more sulfate resistance than Portland concrete glue in a folio.
Besides, consolidation of KOH (12 M) activated fly ash in gypseous soil recorded the most
minimal collapsible potential and coefficient of penetrability at 90 days of presentation.
This examination explores by Itthikorn Phummiphan et al in 2017 [24]. The impacts of
alkali activator and curing time on Unconfined Compressive Strength (UCS) and
microstructural attributes of negligible Lateritic Soil (LS) balanced out with high calcium fly
ash (FA)- based geopolymer, which is novel in the field of asphalt geotechnics. The
practicality of utilizing this balanced out material as a bound asphalt material was additionally
assessed through research facility assessment tests. The most extreme early strengths at 7 days
of curing were found at Na2SiO3: NaOH of 90:10, where calcium silicate hydrate (C-S-H),
cementitious items from high calcium FA and Na2SiO3, was found to assume a huge part.
The sodium aluminosilicate hydrate (N-A-S-H) items, being time-subordinate, be that as it
may, became possibly the most important factor after a more extended term.
In 2016 Arul Arulrajah et al. [25] Evaluation of the geopolymer adjustment of C&D
materials with CCR forerunner was likewise contrasted and conventional Fly Ash (FA) and
Slag (S) antecedents. The strength and solidness of the geopolymer balanced out C&D
materials were assessed to discover their application in asphalt bases/subbases. The
consequences of Unconfined Compressive Strength (UCS) and Resilient Modulus (MR)

testing of these geopolymer balanced out CB and RCA totals demonstrate that distinctive
blends of CCR based geopolymers can be utilized to enhance the strength properties of the
C&D totals for asphalt base/subbase applications. CCR + 5% S with the C&D materials
brought about the ideal mix for CCR based geopolymer adjustment of C&D aggregate.
3. PROBLEM IDENTIFICATION
In this area, the paper depicts the portrayal of gypseous soil settled with fly ash gypsum
polymer. In any case, just a couple of components are utilized as a part of training and
concentrated in detail. For the investigation of existing papers a portion of the dangers are
noted and talked about beneath:
 The use of cement to treat the soils with substantial gypsum substance is inclined to
sulfate assault. Amid a dry state, they have high recorded strength; however they are
powerless to expansive decreases in void proportion after wetting which prompts
disappointment [18].
 Increased porosity of the soil alongside breaking down of stabilizers expands the
collapsibility potential of the soil.
 Other than gypseous soil a few soils have trickier. It can grow fall, scatter, and
experience exorbitant settlement, dissolvable or having obvious absence of strength.
 Gypseous soils are normally firm in their dry state yet an incredible loss of strength
and increment in compressibility happen when gypsum is broken down by fractional
or full immersion. This issue turns out to be more extreme when the water moves
through such soils making loss of mass due the leaching of gypsum.
 Based on the analysis of above threats, the systems won't function admirably or make
it more entangled with a specific end goal, to defeat these issues paid the best
approach to proposed technique and examining various approaches.
4. PROPOSED METHODOLOGY
In the proposed study enhances the performance of fly ash geopolymer concrete with the
impacts of intrinsic sulfate in the gypseous soil. This examination primarily decreases the
Collapsibility Potential (Cp) of gypsum soil described by sulfate resistance of geopolymerized
fly ash. Here two gatherings of tests (unsoaked and soaked in MgSO4 solution) were utilized
to test the performance of the structure and to compare these sample with the specimen (OPC
and sulfate resistant cement). These experimental samples are researched by simulation
analysis only in soft computing technique predict experimental results through the use of
measured experimental data. Based on this technology, the Fuzzy algorithm is used to
explore the effect of internal sulfate from gypsum on stabilized soil with varying conditions.
By the relevance of Radial Bias Function (RBF) model needs to diminish the error rate. From
that structure investigated the compression, collapsibility potential for various gypsum
content, Coefficient of Permeability, weight loss, and leach out in percentage. The hybrid
model (FL-RBF) obtains optimal accuracy and high durability of the activated solution.
4.1. Experimental Modeling
In the experimental examination, geopolymerized fly ash was utilized as a binder to
immobilize the gypsum in soil matrix by covering the gypsum particles in soil and to prevent
any contact between the gypsum particles and water. Besides, the lacking calcium structure of
the gel binder can give sulfate resistant properties by which the exposure of the soil to
Collapsibility Potential (Cp) might be lessened. With respect to adjustment of soil tests,

distinctive alkaline activators (KOH or NaOH) were broken down in the solution at an ideal
molarity as acquired for the binder (8M, 10M, and 12 M). The samples were inundated in
MgSO4 5% solution based on ASTM C1012 standards.
4.1.1. Materials Required
Flyash Geopolymer: The overall reason for this investigation is to utilize low calcium fly ash
geopolymer to settle gypseous soil in oreder to lessen its collapsibility after wetting. Fly ash is
a finely isolated mineral upsurge resulting because of the combustion of coal in electric
producing plants. In this investigation, fly ash class F (low calcium) is used and the particles
size of fly ash went from 0.6 to 27.8lm with a surface area of 0.72 m 2/g.
Alkali-activated solution: In this investigation, sodium hydroxide (NaOH) and potassium
hydroxide (KOH) were utilized as alkali activators with a molarity of 8, 10, and 12. The fly
ash was specifically blended with the alkaline activator.
Gypseous soil: Gypseous soil is a collapsible soil, which is taken from arid and semi- arid
locales. It shows sudden volumetric changes in wetting state because of the dissolution of
gypsum which causes uneven settlement or crumbling.
4.1.2. Testing Details
Here the binder is is placed in a cylindrical mold with the dimension of 70mm diameter and
20mm high, and curing for the period of 7, 28 and 90 days before collapsibility and
permeability leaching tests. Two gatherings of specimens (un-soaked and soaked in MgSO4
solution) were tried to assess the compressive strength before and after exposure to sulfate.
The un-soaked group was tried at age of 28 days while the soaked samples were tried at ages
of 7, 28, and 90 days after drenching in the solution. Here, fuzzy logic is used to examine the
performance of intrinsic effect of gypseous soil and the rules are conceived.
4.2. Fuzzy Logic (FL)
FL was imagined as a superior strategy for arranging and handling data yet has ended up
being a great decision for some control system applications since it emulates human control
logic. A fuzzy framework is a numerical model that breaks down input esteems as far as
logical values. The fundamental components of fluffy rationale are fuzzy sets, linguistic
variables, fuzzy rules, membership function and defuzzification [26].
Figure 1 FL Model
4.2.1. Fuzzification
This procedure for clarifying the determination of the relationship of a hard regard for each
fuzzy gathering are included for fuzzification. As well, it shows the procedure for adjusting
the actual scalar quality into a fuzzy value. A fuzzy partition of a gathering X show an
Crisp input
Membership
function
Rule
generation
Defuzzification
Crisp output
TRIMF
Fuzzification
Linguistic variable

endeavor A: X→L, where L stays for the between time [0, 1]. This endeavor is moreover
addressed as a membership function.
Linguistic variables: While variables in mathematics as a rule take numerical esteems, in
fuzzy logic applications, the non-numeric linguistic variables are regularly used to encourage
the statement of rules and facts. Here, we need to take inputs as length, diameter, varying
temperatures, alkali activators i.e. NaOH, KOH for molarities [8, 10 and 12], changing flyash
polymers (10%, 20% and 30%) and the output is compressive strength, weight loss, Cp,
C.O.P, leachout for differing gypsum contents (G13, G25, and G45) in view of 7, 28, 90 days.
The input factors and the output variables must be changed over to the related linguistic
variables, for example, 'high', 'medium', 'low' and ‘fast’, 'medium' or slow.
4.2.2. Rules and Membership Function
The two elements are critical in fuzzy logic based frameworks. We can partition the input and
output parameters into various extents with the assistance of membership functions. From that
rule formation, is done i.e. characterize certain rules or conditions, based on that system
investigation is finished.
Membership function: A membership function is talented by various shapes for the
evaluation in the fuzzy reason the most easy membership undertaking is made by applying to
straight lines. The simplest difficult different membership function is connected from among
them. In this examination, Triangular Membership Function (TRIMF) is utilized to make the
standards in FL and clarified as underneath:
Triangular Membership Function (TRIMF)
A triangular MF is specified by three parameters {a, b, c} as follows



















cx
bxbbcxc
bxaabax
ax
cbaxtriangle
0
)/()(
)/()(
0
),,:( (1)
The parameters {a, b, c} (with a < b < c) decide the x directions of the three corners of the
fundamental triangular MF. Despite the fact that in the required membership function the
rules are conveyed, by input and output the directions are made discretely and the strategy is
refined in the fuzzy rationale controller. From the correct information and output pair of
availability data, a resultant genuine esteem is accomplished. For each fuzzy if-then rule
produced using the fuzzy subspaces is begun on the supposition that the field space of every
data variable is apportioned evenhandedly into fuzzy groups.
Rule generation process:The Set of IF-THEN principles are building to establish the
coveted behavior of the framework based on information of the human master. On the off
chance that x is A THEN y is B, Where A and B are linguistic values of the linguistic
variables x and y, individually. In order to achieve the full functionality of the framework, the
standards can be continued modifying. Test fuzzy principles are demonstrated as follows.
IF THEN
Alkali activator has high molarity, curing days
has high
Compressive strength is
increased
Temperature is high, alkali activator is high Mass loss of the specimen is
reduced
Gypsum content is low, flyash geopolymer
content is high
Cp is highly reduced
Gypsum content is high, curing days is high or Cp is very high

medium
Time is high, flyash is high Leaching in sol is high
Soil is treated, flyash polymer content has
medium or high %
C.O.P is medium
4.2.3. Defuzzification
Defuzzification is performed by the membership function of the output variable. It is the way
toward changing a fuzzy output of a fuzzy inference framework into a crisp output. The last
stage output of the fuzzy lessens the inherent impacts of gypseous soil and decreases the
collapsibility potential yet to enhance the yield we have use Radial Bias Function (RBF). RBF
limits the error rate and trained the fuzzy structure with the optimal output.
4.3. Radial Bias Function (RBF)
A RBF neural network comprises of an input and output nodes and a single hidden layer.
Every node in the hidden layer executes a bias function and the number of hidden nodes is
equivalent to the quantity of information focuses in the training database. The RBF
approximates the obscure function that maps the contribution to the output regarding a
premise function extension. The neurons in the hidden layer contain Gaussian transfer
functions whose outputs are contrarily corresponding to the separation from the center point
of the neuron [27].
4.3.1. Objective Function
The hidden layer adjusts the data from the input space to the hidden space with the assistance
of a non- linear function. The output layer is linear and returns the reaction of the network. A
standardized Gaussian function commonly is used as the radial basis function as beneath:
  






 
 2
2
exp,
i
i
ii
r
cm
cm
(2)
Where m represents theinput parameters, ir
signify the radius of the
th
i node, ic
center vector for the neuron. It has been confirmed that if enough units are conveyed, a RBF-
NN can uncertain any multivariate continuous function as proposed. Along these lines, the
fundamental issues in RBF NNs plan issues instate the quantity of hidden neurons to use and
their centers and radii. The output of the RBF-NN is ascertained by underneath condition
    mtcpWcpWRBF
N
i
iiti
N
i
iiti ,...,2,1,
1
2
1
  
 (3)
Where,
1
 n
Rx is an input vector, .k is a radial basis function from
1n
R (set of all
positive real number) to R , 2
.
signifies the Euclidean norm, tiW
are the weights of the links
that attaches hidden neuron number i and output neuron number t in the output layer, N is the
sum of neurons in the hidden layer and
1
 n
i Rc
are the RBF centers in the input vector space.
4.3.2. Training RBF Networks
The network training is partitioned into two phases: to begin with, the weights from the input
to hidden layer are resolved, and after that the weights from the hidden to the output layer.
The weights associating the fundamental units to the outputs are utilized to take linear blends
of the hidden units to create the last characterization or output.

Adjusting the widths:In its least complex frame, every single hidden unit in the RBF
network has a similar width or level of sensitivity to inputs. Figuring these individual widths
expands the execution of the RBF network to the detriment of a more confounded training
process.
Adjusting the centers: In radial basis function networks, in any case, the weights into the
hidden layer premise units are generally set before the second layer of weights is balanced. As
the input moves from the association weights, the activation value tumbles off. Regardless,
they are used to set the zones of sensitivity for the RBF network's hidden units, which at that
point remain settled.
Adjusting the weights: Once the hidden layer weights are set, the second period of
training is utilized to change the output weights
Finally testing the performance of the RBF network, RBF values are formed from the
input data and processed with the final weights obtained during training. Based on the result
obtained, the outputs are optimally and improve the performance of the innovative model.
4.4. Fuzzy-Radial Bias Function (FL-RBF)
The design and implementation of fuzzy frameworks presented by radial bias function have
turned out to be exceptionally dynamic regions of research. In the proposed study, the output
from the fuzzy is trained by the radial bias function in the hidden layer and after that finds the
ideal output. The output layer can limit the error rate and ideally dissect the impacts of
intrinsic sulfate attack in the gypseous soil. Figure 2 demonstrates the diagrammatic model for
our proposed hybrid approach.
Figure 2 hybrid model (Fuzzy-RBF)
5. RESULT AND ANALYSIS
This proposed approach is implemented utilizing the working stage of MATLAB 2015 with
the system configuration, i5 processors with 4GB RAM. Approval of numerical outcomes for
the effects of intrinsic sulfate in gypseous soil on its collapsibility potential when stabilized
with fly ash geopolymer binder was performed with the experimental data. As indicated by
the outcomes, this area examined the effect of parameters, compared with hybrid (Fuzzy-
RBF) to isolate algorithms (Fuzzy and RBF). The outcomes are concluded by utilizing tables,
and bar graphs, and so on.
Table 1 Parameter analysis
Curing
days
Activated
solution [M]
Compressive strength (Mpa) (Soaked) Weight loss (%)
KOH NaOH Actual Fuzzy RBF
(Fuzzy-
RBF)
MSE Actual Fuzzy RBF
(Fuzzy-
RBF)
MSE
7
8 -
25.7 27.67 28.64 27.44 1.74 1.5 2.59 3.15 3.72 1.09
28 24.3 25.97 25.85 26.25 1.55 3.2 6.26 6.27 5.61 1.12
90 20.36 32.44 23.09 22.20 1.84 5.4 8.66 7.81 8.63 2.41
7
10 -
27.6 28.71 29.18 30.97 1.11 0.9 3.63 4.57 3.17 1.75
28 26.3 29.15 28.33 28.52 2.03 2.7 5.96 5.63 6.58 1.16
90 24.76 32.57 27.21 28.22 1.55 4.9 6.67 8.71 7.43 1.77
7 12 - 33.6 30.03 37.25 36.69 0.90 0.3 3.51 3.78 3.49 1.88
Fuzzy
Crisp
output
RBF
Trained
structure
Minimize the
error rate

28 28.5 31.01 32.02 30.63 2.13 1.3 4.16 3.06 3.18 1.76
90 27.3 32.59 29.67 30.51 1.66 4.6 4.28 7.02 7.20 0.32
7
- 8
25.6 31.05 29.54 28.01 2.13 1.6 4.30 5.57 3.86 2.26
28 22.3 31.03 24.13 24.45 1.83 3.9 4.30 6.89 6.26 0.40
90 19.6 20.88 22.45 21.34 1.28 6.3 9.21 8.16 8.41 1.86
7
- 10
26.6 31.05 29.97 29.44 2.84 0.86 4.30 3.25 2.93 2.07
28 24.2 31.04 27.79 27.32 3.12 3.2 4.30 5.65 6.52 1.10
90 26 20.88 29.87 29.49 1.52 6.2 9.21 9.45 8.65 2.45
7
- 12
28 31.05 30.93 29.68 1.68 0.5 4.30 3.28 2.99 2.49
28 27.3 31.05 29.97 29.69 2.39 2.6 4.30 5.48 5.15 1.70
90 22.6 20.89 25.81 25.49 1.71 5.7 9.21 9.61 9.19 2.87
Table 1 demonstrates the compressive strength and weight loss of various alkali activators
(KOH and NaOH) with shifting molarity. In this C.S is broke down for soaked specimen
based on 7, 28 and 90 days. Here, the trial result is contrasted with simulation analysis
(Fuzzy, RBF, and Fuzzy-RBF). For the examination of these systems, hybrid algorithm
achieves the ideal value contrasted with test results.
Figure 3 Compressive strength analysis
Figure 3 demonstrates the compressive strength analysis of alkali activators as molarities
[8M], [10M] and [12M]. In this investigation, the activated samples with KOH (12 M)
accomplished practically identical strength to that of different samples. In any case, the
samples activated with NaOH of 12 M could accomplish the strength like different samples.
Here, the hybrid procedures accomplish superior than experimental and separate algorithms.
The activation of tests with high molarity solutions expanded the compressive strength;
nonetheless, the KOH appeared to be more successful than NaOH.
Figure 4 Weight loss analysis

Figure 4 delineates the weight loss examination for varying alkali molarities. In this chart
indicates KOH [12M] and NaOH [12M] diminishes the weight loss for every one of the
methods. The most minimal weight loss was gotten in test activated with KOH (12 M) for
which 0.10%, 1.25%, 4.5% were recorded at 7, 28 and 90 days, individually. By and large, all
geopolymer tests activated with KOH kept up their morality superior to different examples
regarding mass. Concerning NaOH activated fly ash, the most suitable molarity was 12 M for
which 0.5%, 2.89%, 5.87% of loss were seen at 7, 28 and 90 days, separately.
Table 2 Optimal values of collapsibility potential for types of gypsum specimens
Curi
ng
days
Activated
solution
[M]
Collapsibility Potential
K
O
H
Na
OH
(Unsoaked) soaked
G13 G25 G45 G13 G25 G45
Actual
Fuzzy-RBF
MSE
Actual
Fuzzy-RBF
MSE
Actual
Fuzzy-RBF
MSE
Actual
Fuzzy-RBF
Error
Actual
Fuzzy-RBF
Error
Actual
Fuzzy-RBF
MSE
7
12 -
1.77
3.65
1.88
4.89
6.57
1.57
6.64
8.71
2.07
0.00
1.91
1.63
0.00
1.92
1.89
0
3.03
3.03
28
1.74
4.45
2.32
4.45
6.65
2.20
5.87
7.98
1.40
3.46
5.92
1.62
8.32
11.57
1.79
9.00
12.24
0.79
90
1.41
3.10
1.69
3.79
5.77
1.98
4.84
7.35
1.52
3.10
5.65
1.49
3.90
6.80
2.36
7.66
9.34
1.16
7
0 12
0.98
3.28
1.52
1.95
4.78
2.62
3.70
5.93
1.06
0.00
2.03
1.49
0.00
1.66
1.66
0.00
2.42
1.66
28
0.67
2.55
1.88
1.46
4.08
1.12
1.92
5.30
1.08
2.60
4.86
1.07
5.11
6.87
1.17
5.91
7.26
1.35
90
0.17
1.80
1.63
0.48
2.58
2.10
0.86
3.41
1.16
0.83
3.36
2.53
1.34
3.64
0.79
1.97
5.22
1.21
Table 2 clarifies the collapsibility potentials of gypseous soil specimens settled with
geopolymer fly ash at 7, 28, 90 days of curing and activated solutions. In this table clarifies
just the ideal esteem taken from reproduction examination. On the off chance that the gypsum
content builds Cp additionally increases. In the meantime, adjustment of soil with geopolymer
fly ash diminished the collapsibility in all specimens. Concerning the part of activators, the
collapse potential uncovered a high decrease when the soil was treated with KOH (12 M)
activated fly ash contrasting with that of NaOH (12 M). In the reproduction investigation the
error rate is diminished in the hybrid strategy (Fuzzy-RBF) contrasted with exploratory and
individual algorithms.
Figure 4 Collapsibility potential for unsoaked and soaked analysis

Figure 4 demonstrates the collapsibility potential for unsoaked and soaked with MgSO4
for varying Gypsum content in view of optimization strategies. For the examination of all the
considerable number of strategies; hybrid (Fuzzy-RBF) demonstrates the Cp has extensive
decrease when the curing time expanded. In the wake of absorbing the specimens in MgSO4
the gypseous soil tests with low gypsum content the collapsibility potential kept up at a low
level particularly when activated with KOH. Despite that, when the gypsum content expanded
to 25% and 45%, higher substance of geopolymer fly ash was expected to keep up the
collapsibility potential at a controlled range.
Figure 5 C.O.P analysis
Figure 6 Leach out analysis
Figure 5 and 6 represents the C.O.P and Leachout investigation for before and after
soaking process. The exhibitions of the specimens are examined by utilizing optimization
procedures. The soaking specimen appeared to build the coefficient of permeability in the
stabilized soil specimen achieved in hybrid model. In the leach out examination, the leaching
amount was influenced by the amount of added substances thusly when the measurements of
stabilizer expanded the leaching amount diminished. In this diagram demonstrates that the
fuzzy with RBF gives gainful impact of geopolymer fly ash on immobilization of gypsum and
diminishment of collapsibility potential in like manner.

Figure 7 MSE analysis
Figure 7 speaks to the MSE analysis for C.S, weight loss, Cp, C.O.P, Leachout. The
proposed MSE result is looked at for exploratory outcomes. MSE is a measure of how close a
fitted line is to data points. The littler the Mean Squared Error, the nearer the fit is to the data.
The MSE investigation depends on testing data for every one of the outputs. For the
correlation of trial, fuzzy, and RBF, the proposed hybrid model (Fuzzy-RBF) achieves the
minimum error in every one of the outputs.
6. CONCLUSIONS
In this examination, the geopolymer binder was initially characterized and after that its
execution in the gypseous soil was researched by utilizing simulation analysis. The test
procedure focused on the sulfate resistance of the binder utilizing strong sulfate solution
(MgSO4) and after that effect of internal sulfate from gypsum on stabilized soil was
inspected. The most extreme reduction was recorded in tests with 30% of geopolymerized fly
ash either with KOH (12 M) or NaOH (12 M). Nevertheless, the most astounding rate of
promote in the collapsible characteristic for the gypseous soil was gotten in tests treated with
fly ash/KOH (12 M) which is accomplished in hybrid model (fuzzy-RBF). The most reduced
collapsible potential was gotten when the gypseous soil was treated with fly ash activated
with KOH instead of NaOH with high molarity. These strategies get optimal accuracy and
high durability of the activated solution. At last, the simulated results give better execution in
hybrid model compared to individual algorithms (fuzzy and RBF). This investigation may
empower and advance further research on the utilization of different geopolymer innovation
in mortar and concrete and the utilization of other alkali-activated materials with different
molarity which will at last prompt improvement of more eco- friendly items with low energy
utilization and low CO2 emissions.

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CHARACTERIZATION OF GYPSEOUS SOIL STABILIZED WITH FLY ASH GEOPOLYMER ANALYZED BY FL-RBF MODEL

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