This paper presents artificial neural network (ANN) based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with twelve different concrete mix proportions. The experimental results are training data to construct the artificial neural network model. The data used in the multilayer feed forward neural network models are arranged in a format of ten input parameters that cover the age of specimen, cement, Fly ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and Superplasticizer. According to these parameter in the neural network models are predicted the compressive strength values of concrete containing Industrial Byproducts. T

1. http://www.iaeme.com/IJCIET/index.asp 302 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 7, Issue 2, March-April 2016, pp. 302–314, Article ID: IJCIET_07_02_026
Available online at
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=2
Journal Impact Factor (2016): 9.7820 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
PREDICTION OF COMPRESSIVE
STRENGTH OF HIGH PERFORMANCE
CONCRETE CONTAINING INDUSTRIAL BY
PRODUCTS USING ARTIFICIAL NEURAL
NETWORKS
Dr. B. Vidivelli
Professor, Department of Civil & Structural Engineering,
A. Jayaranjini
Research Scholar,
Department of Civil & Structural Engineering,
Annamalai University, Tamilnadu, India
ABSTRACT
This paper presents artificial neural network (ANN) based model to
predict the compressive strength of concrete containing Industrial Byproducts
at the age of 28, 56, 90 and 120 days. A total of 71 specimens were casted with
twelve different concrete mix proportions. The experimental results are
training data to construct the artificial neural network model. The data used
in the multilayer feed forward neural network models are arranged in a
format of ten input parameters that cover the age of specimen, cement, Fly
ash, Silica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and
Superplasticizer. According to these parameter in the neural network models
are predicted the compressive strength values of concrete containing
Industrial Byproducts. This study leads to the conclusion that the artificial
neural network (ANN) performed well to predict the compressive strength of
high performance concrete for various curing period.
Key word: Compressive Strength, High Performance Concrete, Industrial by
Products, Neurons, Neural Network.
Cite this Article: Dr. B.Vidivelli and A. Jayaranjini. Prediction of
Compressive Strength of High Performance Concrete Containing Industrial by
products Using Artificial Neural Networks, International Journal of Civil
Engineering and Technology, 7(2), 2016, pp. 302–314.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType=2

2. Prediction of Compressive Strength of High Performance Concrete Containing Industrial by
products Using Artificial Neural Networks
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INTRODUCTION
In view of the global sustainable development, it is imperative that supplementary
cementing materials be used in replace of cement in the concrete industry. The most
worldwide available supplementary cementing materials are silica fume (SF), a by-
product of silicon metal and fly ash (FA), a by-product of thermal power stations, and
blast-furnace slag (BS), a byproduct of steel mill. It is estimated that approximately
600 million tons of FA are available worldwide now, but at present, the current
worldwide utilization rate of FA in concrete is about 10%. However, the recent
development of green high performance concrete (GHPC) brings the abundant
utilization of these mineral mixtures. When these different reactive mineral admix-
tures are added into concrete at the same time, they develop their own characteristics
with the development. SF can increase the strength of the concrete significantly;
however, it affects the workability of the fresh concrete greatly, while adding large
amount of FA to the concrete contributes the workability of the concrete but not to the
strength. In addition, those mineral admixtures show different effects on the strength
of the concrete within different ages due to their different pozzolan reactions. The aim
of this study is to build models which have two different architectures in ANN system
to evaluate the effect of FA, MK, SF and BA on compressive strength of concrete. For
purpose of constructing this models, 12 different mixtures with 36 specimens of the
28 days compressive strength results of concrete containing FA, SF, MK and BA used
in training for ANN system were collected for the Experimental work. In training of
models constituted with different architectures. The age of specimen(AS), Cement(C),
Fly ash (FA), Silica fume(SF), Metakaolin(MK), Sand(S), Bottom ash (BA), Coarse
aggregate (CA), Water(W) and Superplasticizer(SP) were entered as input; while
compressive strength(fc) values were used as output. The models were trained with 71
data of experimental results were obtained.
LITERATURE REVIEW
Noorzaei et al. (2007) focused on development of artificial neural networks (ANNs)
for prediction of compressive strength of concrete after 28 days. To predict the
compressive strength of concrete six input parameters cement, water, silica fume,
super plasticizer, fine aggregate and coarse aggregate were identified considering
two hidden layers for the architecture of neural network. The results of the study
indicated that ANNs have strong potential as a feasible tool for predicting the
compressive strength of concrete. Atici et al., (2009) applies multiple regression
analysis and an artificial neural network in estimating the compressive strength of
concrete that contains varying amounts of blast furnace slag and fly ash. The results
reveal that the artificial neural network models performed better than multiple
regression analysis models. Serkan subas (2009) investigated that the estimation
ability of the effects of utilizing different amount of the class C fly ash on the
mechanical properties of cement using artificial neural network and regression
methods. Experimental results were used in the estimation methods. The developed
models and the experimental results were compared in the testing data set. As a result,
compressive and flexural tensile strength values of mortars containing various
amounts class C fly ash can be predicted in a quite short period of time with tiny error
rates by using the multilayer feed-forward neural network models than regression
techniques. Seyed et.al (2011) studied the application of artificial neural networks to
predict compressive strength of high strength concrete (HSC). A total of 368 different
data of HSC mix-designs were collected from technical literature. The authors
concluded that that the relative percentage error (RPE) for the training set was 7.02%

3. Dr. B.Vidivelli and A. Jayaranjini
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and the testing set was 12.64%. The ANNs models give high prediction accuracy, and
the research results demonstrate that using ANNs to predict concrete strength is
practical and beneficial. Vijay et al., (2013) predicted the compressive strength of
concrete using Artificial Neural Network (ANN). The authors compared the predicted
compressive strength with the obtained actual compressive strength of concrete and
also the authors proposed equations for different models. The authors concluded that a
good co-relation has been obtained between the predicted compressive strength by
these models and experimental results. Sakshigupta et.al., (2013) used Artificial
Neural Network (ANN) to predict the compressive strength of concrete containing
nano-silica. The author developed a model for predicting 28 days compressive
strength of concrete with partial replacement of cement with nano-silica for which the
data has been taken from various literatures. The author concluded that compressive
strength values of concrete can be predicted in ANN models without attempting any
experiments in a quite short period of time with some error rates. Wankhade et.al,
(2013) used Artificial Neural Network (ANN) to predict the compressive strength
of concrete. To train the networks back propagation and Jordan–Elman algorithms
are used. Networks are trained and tested at various learning rate and momentum
factor and after many trials these were kept constant for this study. Performance of
networks were checked with statistical error criteria of correlation coefficient, root
mean squared error and mean absolute error. The authors concluded that artificial
neural networks can predict compressive strength of concrete with 91 to 98 %
accuracy.
EXPERIMENTAL INVESTIGATION
M30 grade of concrete were used for the present investigation. Mix design was done
based on IS 10262 – 2009 (17). The concrete mix proportion 1:1.73:3.2 with w/c 0.45
considered in this study. Twelve HPC mixes were prepared for this test by volumetric
method. The conventional concrete mix CC and Combinations of HPC mixes (S1-
S11) as given in Table.1. The volume of water is 172.8 lit/m3
and Coarse aggregate
(CA) is 1220 kg/m3
were kept constant while the volume of cement, sand and
Superplasticizer (SP) were varied for all the mixes. The mix Combinations and mix
proportions are given in table 1 & 2. The selected 4 HPC mixes are having the
maximum compressive strength at 28 days including CC & S3, S7, S10 and S11.
PREPARATION OF TEST SPECIMEN
Concrete cubes and cylinders were casted for all five mixes. For each combination,
trial mixes were carried out. In total 71 were casted for all mixes. All the materials
were thoroughly mixed in dry state by machine so as to obtain uniform colour. The
required percentage of superplasticizer was added to the water calculated for the
particular mix. The slump tests were carried out on fresh concrete for all the mixes.
The entire test Specimens were cast using Standard steel mould and the concrete were
compacted on a vibrating table. The specimens were demoulded after 24 hours and
cured in water for 28 days. The test results were carried out confirming to IS 516-
1959 (16) to obtain compressive strength of concrete. The cubes were tested using
compression testing machine (CTM) of capacity of 2000KN.

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Table 1 Combinations of Mixes
S.No
Mixdesignation
Combinations
1 CC (C+S+CA)
2 S1 (C+FA20%)+S+CA)
3 S2 (C+SF10%)+S+CA
4 S3 (C+MK10%)+S+CA
5 S4 C+(S+BA20%)+CA
6 S5 (C+FA20%)+(S+BA20%)+CA)
7 S6 (C+SF10%)+(S+BA20%)+CA
8 S7 (C+MK10%)+(S+BA20%)+CA
9 S8 (C+FA20%+SF10%)+(S+BA20%)+CA
10 S9 (C+FA20%+MK10%)+(S+BA20%)+CA
11 S10 (C+SF10%+MK10%)+(S+BA20%)+CA
12 S11 (C+FA20%+SF10%+MK10%)+(S+BA20%)+CA
ARTIFICIAL NEURAL NETWORK
Artificial neural network are nonlinear information (signal) processing devices, which
are built from interconnected elementary processing devices called neurons. An
artificial neural network (ANN) is an information processing paradigm that is inspired
by the way biological nervous system such as the brain, process information. The key
element of this paradigm is the novel structure of the information processing system.
It is composed of a large number of highly interconnected processing elements
(neurons) working in union to solve specific problems. An ANN is configured for a
specific application, such as pattern recognition or data classification, through a
learning process. Learning is biological systems involves adjustments to the synaptic
connection that exist between the neurons. ANN’s are a type of artificial intelligence
that attempts to imitate the way a human brain works. Rather than using a digital
model, in which all computations manipulate zeros and ones, a neural networks by
creating connection between processing elements, the computer equivalent of
neurons. The organization and weights of the connections determine the output.

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Table.2 Proportion of mixes
Figure 1 The System used in ANN-I model
S.N
o
Mix
Cement(C
) (kg/m3
)
Fly ash
(FA)
(kg/m3
)
Silica
fume
(SF)
(kg/m3
)
Metakao
lin (MK)
(kg/m3
)
Fine
aggregat
e (S)
(kg/m3
)
Bottom
ash
(BA)
(kg/m3
)
SP
(lit/m3
)
Slum
p
(mm)
Experimental
Compressive
strength for
28
days(N/mm2
)
1 CC 384 0 0 0 665 0 2.49 55 36.5
2 S1 307.2 76.8 0 0 623 0 3.37 57 34.54
3 S2 345.6 0 38.4 0 644 0 3.97 55 37.03
4 S3 345.6 0 0 38.4 649 0 3.45 56 41.34
5 S4 384 0 0 0 508 133 3.84 58 35.33
6 S5 307.2 76.8 0 0 476 133 3.99 55 37.21
7 S6 345.6 0 38.4 0 500 133 4.83 52 39.31
8 S7 345.6 0 0 38.4 508 133 4.49 57 42.79
9 S8 268.8 76.8 38.4 0 461 133 4.03 59 37.25
10 S9 268.8 76.8 0 38.4 467 133 3.49 58 39.22
11 S10 307.2 0 38.4 38.4 492 133 4.60 57 44.69
12 S11 230.4 76.8 38.4 38.4 463 133 3.80 58 40.0
A
S
F
A
S
F
M
KF
B
A
W
S
C
C
A
S
P
Input
layer
1. Hidden
layer
N
1
N
3
N
4
N
5
N
6
N
7
N
8
N
2
N
9
N10
fc

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Figure 2 The System used in ANN-II model
FEED FORWARD NEURAL NETWORK
In a feed forward neural network, the artificial neurons are arranged in layers, and all
the neurons in each layer have connections to all the neurons in the next layer.
However, there is no connection between neurons of the same layer or the neurons
which are not in successive layers. The feed forward network consists of one input
layer, one or two hidden layers and one output layer of neurons.
Input
layer
1.Hidden
layer
A
S
F
A
S
F
M
K
B
A
W
S
C
C
A
S
P
N1
N2
N3
N4
N5
N6
N7
N8
N9
N10
N11
N12
N13
N14
N15
N16
N17
N18
N19
N20
2.Hidden
layer
fc
3.Output
layer

7. Dr. B.Vidivelli and A. Jayaranjini
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Table 3 The Input and Output quantities used in ANN model.
Datas
Data used in training the models
Minimum Maximum
Input Variables
Age of Specimen (day) 28 120
Cement (Kg/m3
) 230.4 384
Silica fume (Kg/m3
) 0 38.4
Metakaolin (Kg/m3
) 0 38.4
Fly ash (Kg/m3
) 0 76.8
Bottom ash (Kg/m3
) 0 133
Sand (Kg/m3
) 461 665
Coarse Aggregate (Kg/m3
) 1220 1220
Superplasticizer (l/m3
) 2.49 4.6
Output variable
Compressive strength 36.31 47.38
Table 4 Experimental results with Predicted results from models for 28 days
Compressive strength (N/mm2
) 28 days
Mix Designation Experimental result ANN-I ANN-II % Error
CC-1 36.310 36.823 36.452 -0.513
CC-2 36.890 36.823 36.452 0.067
CC-3 36.620 36.823 36.452 -0.203
S3-1 41.020 41.493 41.302 -0.473
S3-2 41.330 41.493 41.302 -0.163
S3-3 41.690 41.493 41.302 0.197
S7-1 42.040 43.225 42.951 -1.185
S7-2 42.310 43.225 42.951 -0.915
S7-3 44.040 43.225 42.951 0.815
S10-1 43.960 44.124 43.950 -0.164
S10-2 44.090 44.124 43.950 -0.034
S10-3 46.040 44.124 43.950 1.916
S11-1 38.980 39.980 39.951 -1.000
S11-2 39.980 39.980 39.951 0.000
S11-3 41.020 39.980 39.951 1.040

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Table 5 Experimental results with Predicted results from models for 56 days
Table 6 Experimental results with Predicted results from models for 90 days
Compressive strength (N/mm2
) 56 days
Mix
Designation
Experimental
result
ANN-I ANN-II % Error
CC-1 36.670 36.814 37.094 -0.424
CC-2 36.970 36.814 37.094 -0.124
CC-3 36.620 36.814 37.094 -0.474
S3-1 42.500 42.513 42.941 -0.441
S3-2 41.500 42.513 42.941 -1.441
S3-3 43.500 42.513 42.941 0.559
S7-1 44.100 43.936 44.071 0.029
S7-2 43.200 43.936 44.071 -0.871
S7-3 44.500 43.936 44.071 0.429
S10-1 46.800 45.485 45.456 1.315
S10-2 44.700 45.485 45.456 -0.785
S10-3 45.000 45.485 45.456 -0.485
S11-1 41.000 41.254 40.790 -0.254
S11-2 39.500 41.254 40.790 -1.754
S11-3 41.500 41.254 40.790 0.246
Compressive strength (N/mm2
) 90 days
Mix Designation
Experimental
result ANN-I ANN-II % Error
CC-1 37.110 37.750 37.242 -0.640
CC-2 37.280 37.750 37.242 -0.470
CC-3 37.200 37.750 37.242 -0.550
S3-1 43.200 43.662 43.200 -0.462
S3-2 42.500 43.662 43.200 -1.162
S3-3 44.300 43.662 43.200 0.638
S7-1 44.600 44.652 44.856 -0.256
S7-2 44.100 44.652 44.856 -0.756
S7-3 45.800 44.652 44.856 0.944
S10-1 46.540 46.639 46.160 -0.099
S10-2 45.820 46.639 46.160 -0.819
S10-3 45.970 46.639 46.160 -0.669
S11-1 41.520 42.446 41.761 -0.926
S11-2 41.360 42.446 41.761 -1.086
S11-3 42.380 42.446 41.761 -0.066

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Table 7 Experimental results with Predicted results from models for 120 days
Two different multilayer artificial neural network architectures namely ANN-I
and ANN-II were built. In training and testing of the ANN-I and ANN-II models
constituted with two different architectures AS, C, FA, SF, MK, BA, S, CA, W and
SP were input values, while fc value were used as output. In the ANN-I & ANN-II, 71
data of experimental results were used for training. In ANN-I model, one hidden layer
were selected as shown in fig.1 In the hidden layer 10 neurons were determined due to
its minimum absolute percentage error values for training sets. In ANN-II model, two
hidden layers were selected as shown in fig.2. In the first hidden layer 10 neurons and
in the second hidden layer 10 neurons were determined due to its minimum absolute
percentage error values for training sets. In the ANN-I and ANN-II models, the
neurons of neighboring layers are fully interconnected by weights. Finally the output
layer neuron produces the network prediction as a result. Momentum rate, learning
rate, error after learning cycle were determined for both models were trained through
iterations. The trained models were only tested with the input values and the results
found were close to experimental results.
Figure 3 Experimental Results with training results of ANN-I
Compressive strength (N/mm2
) 120 days
Mix
Designation
Experimental
result ANN-I ANN-II % Error
CC-1 37.550 37.628 37.622 -0.078
CC-2 37.680 37.628 37.622 0.052
CC-3 37.600 37.628 37.622 -0.028
S3-1 44.580 43.323 44.329 0.251
S3-2 42.500 43.323 44.329 -1.829
S3-3 44.170 43.323 44.329 -0.159
S7-1 45.310 45.142 44.846 0.168
S7-2 44.150 45.142 44.846 -0.992
S7-3 45.160 45.142 44.846 0.018
S10-1 47.380 46.650 46.306 0.730
S10-2 46.390 46.650 46.306 -0.260
S10-3 46.250 46.650 46.306 -0.400
S11-1 42.530 42.765 42.361 -0.235
S11-2 42.160 42.765 42.361 -0.605
S11-3 43.000 42.765 42.361 0.235

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Figure 4 Experimental Results with training results of ANN-I
Figure 5 Experimental Results with training results of ANN-I
Figure 6 Experimental Results with training results of ANN-I

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RESULTS AND DISCUSSION
In the training of ANN-I and ANN-II models, various experimental data are used. In
the ANN-I and ANN-II models, 71 data of Experimental results were used for
training. All results obtained from experimental studies and predicted by using the
training results of ANN-I models for 28, 56, 90 and 120 days fc were given in fig.3, 4,
5 and 6 respectively. The linear least square fit line, its equation and the R2
values
were shown in these figures for the training data. Also, input values and Experimental
results with training results obtained from ANN-I and ANN-II models were given in
table.1, 4, 5, 6 and 7. The results of training phase in Fig.3, 4, 5 and 6 shows that
these models are capable of generalized between input and output variables with
reasonably good predictions.
The statistical values of all the values such as Root Mean Square (RMS), Mean
Square Error (MSE), Mean Absolute Percentage Error (MAPE) and R2
training are
given in table. While these values of RMS, MSE, MAPE and R2
from training in the
ANN-I model were found as 0.787, 0.619, 1.384% and 99.9% respectively. The best
value of R2
is 99.9% for training set in the ANN-I model. The minimum value of R2
is
99.6% for training set in the ANN-I model. All of the statistical value in table 9 show
that the proposed ANN-I and ANN-II models are suitable and predict the 28, 56, 90
and 180 days compressive strength (fc) values are very close to the experimental
values.
Table.8 The fc statistical values of proposed ANN-I and ANN-II models
CONCLUSIONS
In this Study, using these beneficial properties of artificial neural networks in order to
predict the 28, 56, 90 and 120 days compressive strength values of concrete
containing Industrial Byproducts with attempting experiments were developed two
different architectures namely ANN-I and ANN-II. In two models developed on ANN
method, a multilayer feed forward neural network in a back propagation algorithm
were used. In ANN-I model, one hidden layer were selected. In the hidden layers 10
neurons were determined. In ANN-II model, two hidden layers were selected. In the
first hidden layers 10 neurons and in the second hidden layer 10 neurons were
determined. The models were trained with input and output data. The compressive
strength values predicted from training for ANN-I & ANN-II models were very close
to the experimental results. Furthermore, according to the compressive strength results
predicted by using ANN-I and ANN-II models, the results of ANN-II model are
closer to the experimental results. RMSE, MSE, R2
and MAPE statistical values that
are calculated for comparing experimental results with ANN-I and ANN-II model
results have shown this situation. As a result, compressive strength values of
Statistical
parameter
(Training
set)
28 days 56 days 90 days 120 days
ANN-I
ANN-II
ANN-I
ANN-II
ANN-I
ANN-II
ANN-I
ANN-II
RMSE 0.787 0.790 0.764 0.763 0.716 0.525 0.591 0.626
MSE 0.619 0.625 0.584 0.583 0.513 0.276 0.349 0.392
MAPE
(%)
1.384 1.345 1.370 1.499 1.453 0.939 1.019 0.916
R2 0.999 0.996 0.999 0.999 0.999 0.9997 0.9998 0.999

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concretes containing Industrial Byproducts can be predicted in the multilayer feed
forward artificial neural networks models with attempting experiments in a quite short
period of time with tiny error rates. ANN can be suggested to predict the concrete
compressive strength with high accuracy.
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