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International Journal of Civil Engineering and Technology (IJCIET)

Volume 7, Issue 2, March-April 2016, pp. 302–314, Article ID: IJCIET_07_02_026

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ISSN Print: 0976-6308 and ISSN Online: 0976-6316

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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

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=7&IType

Prediction of Compressive Strength of High Performance Concrete Containing Industrial by

products Using Artificial Neural Networks


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%

Dr. B.Vidivelli and A. Jayaranjini


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.




Table 1 Combinations of Mixes

S.No

Mix

des

ign

ati

on

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.



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




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



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







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


Mix Designation

Experimental


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




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


Mix

Designation

Experimental


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



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 R

2 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

AN

N-I

AN

N-I

I

AN

N-I

AN

N-I

I

AN

N-I

AN

N-I

I

AN

N-I

AN

N-I

I

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




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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