Ijciet 10 01_170

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 01, January 2019, pp. 1835-1850, Article ID: IJCIET_10_01_170
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=01
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
MECHANICAL PROPERTIES OF TERNARY
BLENDED CONCRETE MADE BY MK-FA-GGBS
AND HYBRID FIBERS-EXPERIMENTAL &
SIMULATION APPROACH
Nagaraja K
Research Scholar JNTUA, Ananthapuramu
Dr. H. Sudarshana Rao
Professor and Director of Consultancy wing, JNTUA Ananthapuramu
ABSTRACT
Because of the excellent strength of concrete reinforced with fibers pulled in the
consideration of researchers throughout the most recent decades. The proposed
technique manages the experimental investigation to determine the properties of
Ternary Blended Fiber Reinforced Concrete (TBFRC) with the assistance of soft
computing methodology performed in MATLAB software. In the present experimental
examination a mix design of M50 is tried at utilizing ternary blend of Ground
Granulated Blast Furnace Slag (GGBS), Fly Ash (FA) and Metakaolin (MK) as
partial replacement by weight of concrete at different mixing rates running between
0% – 30% with extra steel and polypropylene fibers. Here, the mechanical properties,
for example, compressive strength, split tensile strength, and flexural strength, are
anticipated by utilizing Deep Learning Neural Network (DNN) strategy with various
fiber rate. The input factors for the neural network depict the materials and different
mix extents of concrete. In this network structure, the weights are enhanced by
utilizing Adaptive Crow Search Algorithm (ACSA). Additionally by utilizing this
system of ternary blended fiber reinforced concrete is delivered at a sensible cost than
that of traditional concrete. In addition, the Optimal DNN predicted the mechanical
properties optimally for all curing days (28, 56, and 90 days) compared with
experimental and existing strategies (ANN).
Keywords: ternary blend, steel and polypropylene fibers, DNN, ACSA, mechanical
properties.

Nagaraja K and Dr. H. Sudarshana Rao
Cite this Article: Nagaraja K and Dr. H. Sudarshana Rao, Mechanical Properties of
Ternary Blended Concrete Made by Mk-Fa-Ggbs and Hybrid Fibers-Experimental &
Simulation Approach, International Journal of Civil Engineering and Technology,
10(1), 2019, pp. 1835-1850.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=01
1. INTRODUCTION
Concrete is the standout amongst the most generally utilized development materials. It is
utilized in an assortment of uses, for example, interstates, elevated structures, dams,
scaffolds, walkways, and private development [1]. The quick advancement of development
industry has expanded the utilization of concrete [2]. Yet, the creation of bond includes the
consumption of common assets and greenhouse gas emanations [3]. Likewise, the creation
cost of concrete is expanding step by step. Along these lines, there is a need to search for
elective materials to bond for use in the development [4]. There is a well-known adage that
broken stone, sand, and concrete make great concrete. However, a similar extent of broken
stone, sand, and bond likewise make horrendous concrete [5]. To make fantastic concrete
now an assortment of imaginative materials, for example, fibers, admixtures and development
synthetic concoctions [6], pozzolanas and diverse concrete making systems are received in
present-day development. The manageability of concrete based materials can be
accomplished by partial supplanting bond by mechanical side-effects, for example, fly ash,
slag, and silica fume [7], the use of which can likewise in part take care of the issue of their
transfer and their conceivable undesirable impacts to nature [8].
The function of these Supplementary Cementitious Materials (SCMs) is currently
expanding enthusiasm because of their capability to lessen the clinker factor of cement [9].
The fibers utilized for reinforced concrete are predominantly Steel Fibers (SF), carbon fibers,
and polymer fibers [10]. Because of the remarkable durability of concrete reinforced with
Polypropylene Fibers (PPF) [11]. In principle, a hybrid framework is a mix of at least two
kinds of fiber in an FRC mixture with a goal of utilizing the strength of one sort of fibred to
enhance the limitation of the other [12]. Alternatively, hybridization enhanced the
compressive strength insignificantly contrasted with mono fibers [13-14]. This examination
explored the mix of various fiber proportions of steel and polypropylene within the sight of
fly ash, Ground Granulated Blast-Furnace Slag (GGBS) and Metakaolin. The utilization of
GGBS aggregates in concrete by substitution of common aggregates is exceptionally
encouraging design since its effect strength is very more than normal aggregate [15].
Combination of steel and polypropylene materials in concrete fundamentally enhance its
dying, plastic settlement, warm and shrinkage strains, and stress fixations forced by outside
restrictions [16, 17]. The research work explored the impacts of a hybrid fiber between
crimped steel fiber and polypropylene fiber on properties including the compressive, flexural,
and splitting tensile strength. The examinations are attempted by experimental and simulation
approach. With the assistance of experimental data, simulation approaches are held by using
Optimal DNN with an optimization algorithm.
The upcoming area of the paper is sorted out as pursues: Section 2 portrays the review of
existing literature related to our proposed model; segment 3 maps the procedure part and the
subsection of segment 3 exhibits the explanatory portion of the proposed model. Area 4
examined the result of experimental and simulation modeling. Segment 4 presents the
conclusion section with a future scope.

Mechanical Properties of Ternary Blended Concrete Made by Mk-Fa-Ggbs and Hybrid
Fibers-Experimental & Simulation Approach
2. RELATED WORKS
In 2017 the mechanical properties of hybrid fiber concrete was examined by Hamdy K.
Shehab El-Din et al. [18]. The authors examined the blend of various fiber extents of steel
and polypropylene within the sight of Metakaolin for a 60 MPa concrete at all out fiber
volume divisions. The expansion of various rates of hybrid fibers to the concrete mixtures
was successful for picking up strength at all testing ages. A connection between's the
compressive strength, split tensile strength, and bond strength was produced. In 2017
Chinmaya Kumar Mahapatra et al. [19] researched properties of cross breed fiber reinforced
self-compacting concrete (HyFRSCC) with CSF and polypropylene fibers (PPF) alongside
class F FA and Colloidal Nano-Silica (CNS). Numerous straight relapse examinations predict
conditions of tensile strength as the capacity of barrel compressive strength, for a blend of
FA, CNS, CSF, and PPF. A well-mannered relationship amongst tried and anticipated
qualities was acquired.
In 2017 Sujjavanich et al. [20] announced the impacts of the association among
metakaolin and fly ash on the microstructure and property advancement of concrete. The mix
with the most astounding loads of the monocarbo-aluminate stage yielded the most
noteworthy long haul strength. An extent of concrete: metakaolin: flyash as 80:10:10 yielded
stamped enhancements in a slump, slump loss, and long haul strength. In 2016 Antonio
Caggiano et al. [21] introduced and talked about the consequences of experimental tests
performed on concrete examples inside reinforced with PF and SF. In particular, examples of
five mixtures (in addition to a reference plain concrete), portrayed by a similar absolute
volume of fibers, however extraordinary divisions of polypropylene and steel fibers, and were
tried under pressure and in bowing. Obviously, the outcomes acquired from pressure tests
featured an immaterial impact of fibers as far as strength and, thus, FRC examples showed a
post-crest reaction more bendable than the existing ones.
In 2017 Graciela Lopez Alvarez et al. [22] examined double and ternary fasteners of
customary Portland cement, metakaolin, and limestone as a conceivable answer
todiminishing the measure of cement content in mortar mixes. Results showed that halfway
substitution of metakaolin in mortar mixtures gives higher compressive strength esteems at
early ages. Flexural strength esteems enhanced by expanding the quantity of SF in mixtures;
varieties in metakaolin and limestone on mixtures appeared not to influence on last flexural
results altogether. In 2017 Khan et al. [23] introduced a complete review of the region of high
and ultra-superior cement-based materials. The synergetic connection between hybrid fibers
has a beneficial effect on cementitious composites. The consolidation of miniaturized scale
and nano-pozzolanic materials, for example, FA and SF has been utilized to grow superior
cementitious composites. In 2014, a hypothetical model dependent on an Artificial Neural
Network (ANN) was introduced by Lee et al. [24] for foreseeing shear strength of thin Fiber
Reinforced Polymer (FRP) reinforced concrete flexural individuals without stirrups.
Correlations between the anticipated qualities with 106 test information’s demonstrated that
the created ANN model brought about enhanced measurable parameters with preferred
precision over other existing conditions. In 2018 Kin Gwn Lore et al. [25] proposed hybrid
design of Convolution Neural Networks (CNN) and Stacked Auto-Encoders (SAE) to gain
proficiency with a grouping of causal activities that nonlinearly change an input visual
pattern or conveyance into an objective visual example or circulation with a similar help and
exhibited its reasonableness in exact structure.
In this paper, we portray the strength and properties of ternary blended concrete with
hybrid fibers. Anyway in existing strategies only a few components are utilized in practice

and concentrated in detail. For the investigation of existing papers a portion of the threats are
noted and examined underneath:
 Traditional concrete may contain dissolvable salts, it might cause blossoming. It can
split effectively and low pliability [18]. There are loads of realistic troubles in the
execution of such traditional structures in concrete. At high temperatures, the
material's strength diminishes and avoidance increments. These materials keep
diverting under overwhelming, supported loads (creep). Loading amid crashes can
harm these materials.
 It would be low effect obstruction, combustibility, maturing and loss of strength after
for a moment. Flexural and shear failures are gotten as exceptionally sudden and
startling in the testing time frame, rough and cataclysmic in reinforced specimens
[20]. In view of the examination of above dangers, the systems won't work
respectably or make it increasingly caught with a specific genuine target, to beat these
issues paid the most ideal approach to manage proposed methodology and looking at
different strategies.
3. A NOVEL TERNARY BLEND AND HYBRID FIBERS-PROPOSED
MODEL
The methodology focused to explore the mechanical behavior of high strength concrete
arranged with high rates of ternary mixes like MK, FA, GGBS and hybrid fibers (crimped
steel and polypropylene) with different volume divisions. The proposed system manages the
experimental investigation to determine the properties of hybrid fiber reinforced concrete
utilizing a ternary mix with the assistance of soft computing methodology performed in
MATLAB software. This paper presents ODNN for the prediction of the functionality of
concrete fusing ternary mix with hybrid fibers. The algorithm examined the performance of
every specimen and predicts the mechanical properties by shifting fiber properties.
3.1. Experimental Investigation
In this experimental investigation, Ternary Blended Fiber Reinforced Concrete (TBFRC) has
been utilized. In our examination TBFRC which has the incorporation of two distinctive
pozzolanic materials with the replacement of 40% OPC and furthermore containing fibers
appropriated haphazardly in the grid of the concrete. The pozzolanic materials like fly ash (0,
10%, 20%, and 30%), metakaolin (0, 10%, 20%, and 30%), GGBS (0, 10%, 20% and 30%)
are utilized in the ternary mix. The hybrid fibers which are utilized in this examination are
crimped steel fibers (0-2%) and polypropylene fibers (0-0.3%). An adequate number of
cubes, cylinders, and prisms are thrown and these examples are tried for the adjustment in
pressure, strain and flexural strength at 28, 56 days and 90 days. Here, the mechanical
properties, for example, compressive strength, tensile strength, and flexural strength are
examined dependent on these three curing days. The TBFRC mixing model is
diagrammatically shown in figure 1 and the specimen details appear in underneath table 1:

Figure 1 TBFRC mix
Table 1 Description of the specimen used in this study
S. No. Type
Specimen
name
Concrete mix Description of specimen
1 S1 CS
MK-Metakaolin
G-Ground Granulated Furnace Slag
FA-Fly ash
HF-Hybrid fiber
HF: CSF-1.5%
PF-0.5%
2
Type 1
TB1 MK0G0FA0
3 TB2 MK10G10FA10
4 TB3 MK20G20FA20
5 TB4 MK30G30FA30
6
Type 2
TB5 MK0G0FA0HF0
7 TB6 MK10G10FA10HF1
3.1.1. Material Properties and Concrete Mix Design
All the experiments that are embraced to decide the characteristics of materials are completed
according to the system given by significant Indian standards. In this examination, the
M50grade of concrete was utilized for the whole investigation. The mixing systems were
performed by the Erntroyshaklock’s strategy. The material utilized in this investigation
comprises of Ordinary Portland Cement (OPC-53 Grade) with the specific gravity is 3.10,
coarse aggregate (It goes through 10 mm IS sieve) and the specific gravity is 2.56, the
specific gravity of manufactured sand is 2.50, to upgrade the new properties of the mixture
are included in TBFRC. The water to cement ratio taken for this mix design is 0.35. For
mixing these materials evenly PAN mixer is used, the mixer is shown in below figure 2.

Figure 2 PAN mixer used in our Experimental Investigation
3.1.2. Fiber properties
3.1.2.1. Crimped steel fiber (CSF)
CSF is low carbon, cold drawn steel wire fibers intended to give concrete temperature and
shrinkage split control, upgraded flexural reinforcement, enhanced shear strength and
increment the break obstruction of concrete.
3.1.2.2. Polypropylene fiber (PPF)
PPF are additionally utilized in showered concrete, to enhance the underlying properties and
to diminish sloughing and bounce back. The expansion of polypropylene fibers in the
concrete did not fundamentally influence the compressive strength and the modulus of
flexibility however they do expand the tensile strength. Splitting tensile strength of PFRC
approx. ranges from 9% to 13% of its compressive strength. Expansion of PP fibers in
concrete expands the splitting tensile strength by approx 20% to 50%. The fiber properties
appear table 2. Figure 3 shows the experimental setup view before strength analysis.
Table 2 Properties of CSF and PF
Fiber
Tensile
strength
(MPa)
Length (mm)
Diameter
(mm)
Aspect ratio
Elastic
modulus
(GPa)
Crimped Sisal
fiber
900 30 1 80 200
Polypropylene
fiber
450 12 0.04 300 4000
3.1.3. Specimen Design
3.1.3.1. Compressive Strength (CS)
Compressive strength tests were directed on standard cubes of dimension 100 x 100 x 100
mm. The mortar specimens were put away in molds for 24 h. Subsequent to de-moulded
cubes are kept in ordinary water for relieving; the compressive strength was tried after the

end of particular curing periods. Totally nine specimens are required for determining the CS
analysis.
3.1.3.2. Split Tensile Strength (STS)
STS was led on 150 x 300 mm cylindrical test specimen to assess the tensile capacity given
by concrete as per explicit standards.
3.1.3.3. Flexural Strength (FS)
FS test was led on prisms of measurements 100mm x 100mm x 500mm for three curing days utilizing
the flexural test machine and the outcomes were tabulated.
Figure 3 Experimental setup view; setup for casting and curing cubes, cylinders and prisms
3.2. Predicting the mechanical properties using ODIN
In the simulation modeling, we present the DNN model for the prediction of mechanical
properties of proposed TBFR concrete consolidating MK, FA, and GGBS. The input factors
for the neural network structure the materials and different mix proportions of concrete. In
this network structure, the weight and bias are upgraded by utilizing Adaptive Crow Search
Optimization Algorithm (ACSO). The optimization algorithm gives better execution and
predicting the mechanical properties optimally.
Deep Learning Neural Network: An artificial neural network model with the different
layers of the hidden units and outputs is named DNN. Additionally, it comprises of both pre-
training (utilizing generative deep belief network or DBN) and fine-tuning stages in its
parameter learning.

1) Pre-training stage
The DBN model licenses the network to create visible activations based on its hidden units'
states that describe the network conviction. Here, we executed the RBM to exercises the
exceeding concern.
Restricted Boltzmann Machine: A RBM is an elite kind of Markov random field that has
one layer of (regularly Bernoulli) stochastic hidden units and one layer of (ordinarily
Bernoulli or Gaussian) stochastic visible units. Figure 4 demonstrates the DNN structure
delineates that the number of input neurons which speaks to the ternary blended material rate,
hybrid fiber mixing extents, loads, and so on and different hidden layers are utilized in DNN
[26-28], and afterward the output layer predicting the CS, STS, and FS.
Input: ni DDDD .............,........., 21
Output:
 FSSTSCSOuti ,,max_
Figure 4 DNN structure
Step 1: Basically we initiate the visible units' that implies the input iD to the training
vector.
       

I
i
J
j
I
i
J
j
jjiijiij bDbDSIbaM
1 1 1 1
, 
(1)
Where ijSI symbolizes the symmetric interaction term among the visible unit iD and the
hidden unit jb , , is the bias term, JI, is the number of visible and hidden units. Among
hidden units in an RBM, there are no direct influences; it is enormously easy to get an
impartial sample of  dataji bD ,
  





 
I
i
jiijij DSIDb
1
1 
(2)
Where  x is the logistic sigmoid function
  xexp1
1

, ji hD , is the unbiased sample.

Step 2: We update the hidden and visible units are parallel in the given obvious and
hidden units. For executing the stochastic steepest ascent in the log likelihood of the
preparation data, this demonstrates the route to a much-uncomplicated learning rule as
    tionreconstrucjsidatajsiij yfyfW 
(3)
Where ijW speaks to the updated weight that accomplishes from the altered weight in
hidden layer; When the RBM is prepared, a divergent RBM can be "stacked" over it to frame
a multilayer show. The output layer of the effectively prepared layers is locked in as input to
the novel RBM. The refined deep network weights are occupied with preparing a fine-tuning
stage [27].
3.2.1. DL Weight and bias optimization by using ACSO
Optimization system held to improve the weight of DNN structure for upgrading the
performance of the proposedmodel. CSA is a current meta-heuristic algorithm which is
produced by Askarzadeh, which is motivated on the knowledge-behaviorof crows [29-31].
The CSA transformative process copies the behavior directed by crows of stowing away and
recouping the additional food. In view of the crow's behavior, the standards of CSA are
portrayed as: (a) They live as a flock, (b) they remember food concealing spots, (c) to do a
robbery, they pursue each other alongside (d) by likelihood, and they shield their stores from
being stolen.
Crow Population Initialization: Initialize the number of inhabitants in crows (here, the
weight of the DNN structure) is characterized regarding iW . In a d dimensional condition with
various N crows (rush size), the position of each crow i at the time(iteration) in the search
space is indicated by a vector w
 iterationm
d
iterationmiterationmiterationm
i wwwW ,,
2
,
1
,
..,.........,, (4)
This vector )(w demonstrates the irregular initial position which implies the different
weights. Each crow in the flock has the memory that is utilized to store the best position of its
concealing spots. In these emphases, two conditions may occur.
Fitness function:Here, the contribution of optimization is to attain optimal weights for
DNN structure.
 wOptfuctionObjective _ (5)
Where w signifies the hidden layer weights
Condition 1: Crow y does not realize that crow x is tailing it. Therefore, crows x will way
to deal with the concealing spot of crow y . For this situation, the new position of crow x is
acquired as pursuing:
  iterationmiterationniterationi
lengthi
iterationmiterationn
wmfrandwW ,,,,1,

(6)
Where irand implies the arbitrary number with uniform distribution somewhere in the
range of 0 and 1; i
lengthf shows the flight length of crow m at emphasis.
Condition 2:Generate new position: Crow nrealizes that crow m is tailing it.
Accordingly, so as to shield its store from being stolen, crow n will trick crow m by heading
off to another position of the search space. States 1 and 2 can be communicated as pursues:

 







 

otherwisepositionrandom
APrandifwmfrandw
W
iterationnniterationmiterationn
n
iterationm
lengthi
iterationm
iterationm
,,,,,
1,
(7)
In this algorithm, the increase and expansion are for the most part controlled by the
parameter of Awareness Probability AP . If the new position of a crow is achievable, the crow
refreshes its position. Something different, the crow stays in the present position and does not
move to the produced new position. Pursued by, the fitness value for the refreshed position is
resolved.
ACSA: Crows having better fitness will have optimal awareness probability of updating
its position. In the adaptive formation of CSA principle, the probability of crows’ random
position can be defined as:





 
max
13.0
iteration
iterationP
(8)
Where 'P' characterizes the probability of the arrangement; this algorithm is iteratively
connected for various iterations planning to meet to an adequate solution. It merits
referencing that the arbitrary position of the crow is restricted by the exploration rate at the
present iteration.
Memory updation: Each crow's memory is refreshed likewise dependent on the fitness
estimations of the new position as pursue:



 



otherwiseM
mfwfw
M iterationm
iterationniterniterationm
iterm
,
,1,1,
1, )()(
(9)
At that point, the crows refresh their positions at each iteration penetrating for their best
food (arrangement of the optimization problems). This procedure is rehashed for every one of
the iterations until the point when greatest iteration is come to.
2) Fine tuning phase
The fine-tuning stage is essentially the standard backpropagation algorithm. To classify the
execution of the framework, an output layer is proposed in the highest point of the DNN.
Likewise, the training dataset is talented until the point that the optimized weight is grasped
or elite is accomplished with the assistance of condition (3).
3)DNN output (optimal solution)-ODNN:
The primary contribution of our DNN is to predict the mechanical properties with the
help of condition (3). Also, the training dataset is capable until the point that the optimized
weight is gotten, or most extreme exactness is practiced with the help of the condition.
Finally, in view of the optimal weight (w) in the output layer, the properties are anticipated in
the testing stage by testing the differed input parameters.
4. COMPUTATIONAL RESULTS
In this section discussed the result of experimental and simulation modeling of proposed
TBFRC. The simulation procedure is completed in the working stage of MATLAB 2016a
with 4GB RAM and i5 processor. Approval of the simulation results for the CS, STS, and FS
of various examples was performed utilizing experimental data. The anticipated outcomes are
contrasted and experimental and other existing algorithms.

4.1. Validation result
Table 3 Validation result of compressive strength for all specimens
Specimen
Compressive strength
Experimental ANN ODNN
28 days 56 days 90 days 28 days 56 days 90 days 28 days 56 days 90 days
S1 24.35 44.96 55.35 28.96 53.90 65.43 28.89 57.86 64.29
TB1 27.09 46.96 59.80 29.39 51.12 63.77 30.84 48.57 60.84
TB2 28.54 50.65 59.35 27.79 51.64 65.43 28.53 50.23 64.29
TB3 29.63 52.05 62.26 27.15 52.33 62.93 29.78 52.89 59.12
TB4 30.02 52.65 65.80 28.32 53.68 73.99 29.30 53.88 75.62
TB5 29.99 46.98 59.05 29.49 49.77 64.60 29.66 47.58 62.57
TB6 30.89 48.25 65.80 30.02 52.69 73.99 30.43 53.54 75.62
TB7 31.72 52.78 68.09 30.03 47.73 68.58 29.59 43.92 69.19
TB8 32.91 56.96 73.27 28.85 54.72 70.25 30.91 57.19 72.64
Table 4 Validation result of Split Tensile strength for all specimens
Specimen
Split tensile strength
S1 4.71 6.32 7.06 5.79 7.58 7.03 5.46 7.60 7.84
TB1 5.30 6.85 7.25 5.37 7.82 8.05 5.42 7.43 8.09
TB2 5.40 7.10 7.50 5.58 7.75 7.80 5.54 7.44 8.03
TB3 5.52 7.52 7.99 5.62 7.68 7.55 5.55 7.45 7.97
TB4 5.59 7.82 8.32 5.67 8.00 9.97 5.56 7.78 8.66
TB5 5.06 6.92 7.14 5.28 7.37 8.30 5.41 7.11 8.15
TB6 5.32 7.26 7.99 5.88 7.07 7.55 5.47 7.29 7.97
TB7 6.06 7.60 8.15 5.63 7.45 8.32 5.34 7.62 8.16
TB8 6.35 8.14 8.65 5.78 7.20 9.72 5.67 7.27 8.60

Table 5 Validation result of flexural strength for all specimens
Specimen
Flexural strength
S1 7.80 8.47 10.85 8.81 9.96 11.22 8.87 10.26 11.51
TB1 8.39 9.68 11.14 8.26 9.35 11.85 8.41 9.53 11.96
TB2 9.01 10.18 11.95 9.50 11.22 11.03 8.96 10.51 11.02
TB3 9.34 10.69 12.06 11.52 11.93 11.76 10.38 11.20 12.07
TB4 9.65 11.62 12.36 9.60 11.48 12.93 9.73 11.61 12.35
TB5 9.10 10.67 11.10 10.73 12.64 10.94 9.52 11.89 11.13
TB6 9.65 11.68 11.44 9.05 10.99 12.21 9.27 10.71 12.02
TB7 10.99 11.74 12.74 12.31 10.90 11.66 11.24 10.75 11.46
TB8 11.06 12.84 12.85 11.08 13.03 12.48 10.68 12.82 12.40
(a) (b)
(c)

Figure 5 Mechanical properties of the entire specimen with curing days for proposed DNN model (a)
CS, (b) STS (c) FS
Table 3, 4, and 5 show the validation results which is achieved in our investigation
procedure. In tables, the result of compressive strength split tensile strength and flexural
strength are shown for experimental and Proposed ODNN. This table compares the proposed
model into existing techniques (ANN) which depicted for three curing days. The mechanical
strength increased if the ternary blend increased. The TBFRC range with the specimen of
15% in blended concrete with hybrid fibers 0.5 and 1.5% performs optimal strength
compared to other blended specimens. The prediction result predicts the results optimally in
proposed ODNN model compared to ANN and experimental results. The prediction
procedure is however easy when no need to make expensive and computational time of
mechanical properties. Those results are graphically represented in figures 5 (a), (b) and (c).
(a) (b)
(c)
Figure 6 Comparison analysis of mechanical properties based on optimization techniques (a) CS (b)
STS (c) FS
Figure 6 depicts the comparison analysis of all the optimization techniques for
mechanical strength. The bar graph shows the error value of optimization techniques when
compared to experimental modeling. In CS analysis, DNN with ACSO i.e. ODNN performs

minimum error that means predicted values is nearly equal to experimental value. In 28 days
testing, the minimum error attains 2.8 %, 56 days testing 3.4, 90 days testing 4.3% in ODNN,
similarly, other properties also reaches minimum error in ODNN only.
5. CONCLUSION
In this study proposed the experimental and simulation modeling of TBFRC model by
partially replacing OPC 53 grade cement with up to 10-30% of FA, GGBS, and MK in
various combinations added with hybrid fibers such as CSF and PF. Expansion of hybrid
fibers to all the mixes plainly demonstrate that improvements in all the mechanical properties,
for example, CS, STS and above all expanded FS, this property is extremely helpful in
detaining the cracks to a huge extent. TBFRC has higher strength as well as numerous other
useful properties like better sturdiness, better break opposition, low porousness, cost
adequacy and so on. The 20% replacement, for example, TB7 in type 2 with blended
materials and the optimal mix of cross breed fibers are CSF as 0.5% and PF as 1.5% is the
most ideal replacement, upgrading the concrete's mechanical strength at all ages. As per the
approval result, the proposed prediction process (ODNN) accomplished optimal strength that
implies greatest strength optimization with ANN and experimental model for each curing
days (28, 56 and 90 days). The streamlining algorithm gives better execution and anticipating
the mechanical properties optimally. The hybrid fibers work in perfect blend and get
enhancements in mechanical performance. The optimization system can improve the
effectiveness of the model just as diminishing the computational expenses.
Further work might be preceded with triple blended concrete mixes utilizing other types
of mineral admixtures. Testing of model components might be directed to survey the flexural
properties like diversions, revolutions, pliability, and crack formation and so on with the
assistance of hybrid algorithms.
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