PREDICTION OF COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE CONTAINING INDUSTRIAL BY PRODUCTS USING ARTIFICIAL NEURAL NETWORKS

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 2, March-pril 2016, pp , rticle ID: IJCIET_07_02_026 vailable online at Journal Impact Factor (2016): (Calculated by GII) I Print: and I Online: IEME Publication PREDICTIO OF COMPREIVE TREGTH OF HIGH PERFORMCE COCRETE COTIIG IDUTRIL BY PRODUCT UIG RTIFICIL EURL ETWORK Dr. B. Vidivelli Professor, Department of Civil & tructural Engineering,. Jayaranjini Research cholar, Department of Civil & tructural Engineering, nnamalai University, Tamilnadu, India BTRCT This paper presents artificial neural network () based model to predict the compressive strength of concrete containing Industrial Byproducts at the age of 28, 56, 90 and 120 days. 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, ilica fume, Metakaolin, bottom ash, sand, Coarse aggregate, water and uperplasticizer. ccording 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 () performed well to predict the compressive strength of high performance concrete for various curing period. Key word: Compressive trength, High Performance Concrete, Industrial by Products, eurons, eural etwork. Cite this rticle: Dr. B.Vidivelli and. Jayaranjini. Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks, International Journal of Civil Engineering and Technology, 7(2), 2016, pp

2 Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks ITRODUCTIO 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 (F), a byproduct of silicon metal and fly ash (F), a by-product of thermal power stations, and blast-furnace slag (B), a byproduct of steel mill. It is estimated that approximately 600 million tons of F are available worldwide now, but at present, the current worldwide utilization rate of F 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 admixtures are added into concrete at the same time, they develop their own characteristics with the development. F can increase the strength of the concrete significantly; however, it affects the workability of the fresh concrete greatly, while adding large amount of F 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 system to evaluate the effect of F, MK, F and B 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 F, F, MK and B used in training for system were collected for the Experimental work. In training of models constituted with different architectures. The age of specimen(), Cement(C), Fly ash (F), ilica fume(f), Metakaolin(MK), and(), Bottom ash (B), Coarse aggregate (C), Water(W) and uperplasticizer(p) were entered as input; while compressive strength(f c ) values were used as output. The models were trained with 71 data of experimental results were obtained. LITERTURE REVIEW oorzaei et al. (2007) focused on development of artificial neural networks (s) 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 s have strong potential as a feasible tool for predicting the compressive strength of concrete. tici 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. erkan 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. s 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. eyed et.al (2011) studied the application of artificial neural networks to predict compressive strength of high strength concrete (HC). total of 368 different data of HC mix-designs were collected from technical literature. The authors concluded that that the relative percentage error (RPE) for the training set was 7.02% 303

3 Dr. B.Vidivelli and. Jayaranjini and the testing set was 12.64%. The s models give high prediction accuracy, and the research results demonstrate that using s to predict concrete strength is practical and beneficial. Vijay et al., (2013) predicted the compressive strength of concrete using rtificial eural etwork (). 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. akshigupta et.al., (2013) used rtificial eural etwork () 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 models without attempting any experiments in a quite short period of time with some error rates. Wankhade et.al, (2013) used rtificial eural etwork () to predict the compressive strength of concrete. To train the networks back propagation and Jordan Elman algorithms are used. etworks 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. EXPERIMETL IVETIGTIO M30 grade of concrete were used for the present investigation. Mix design was done based on I (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 (1-11) as given in Table.1. The volume of water is lit/m 3 and Coarse aggregate (C) is 1220 kg/m 3 were kept constant while the volume of cement, sand and uperplasticizer (P) 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 & 3, 7, 10 and 11. PREPRTIO OF TET PECIME 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. ll 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 pecimens were cast using tandard 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 I (16) to obtain compressive strength of concrete. The cubes were tested using compression testing machine (CTM) of capacity of 2000K

4 Mix designation Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks Table 1 Combinations of Mixes.o Combinations 1 CC (C++C) 2 1 (C+F20%)++C) 3 2 (C+F10%)++C 4 3 (C+MK10%)++C 5 4 C+(+B20%)+C 6 5 (C+F20%)+(+B20%)+C) 7 6 (C+F10%)+(+B20%)+C 8 7 (C+MK10%)+(+B20%)+C 9 8 (C+F20%+F10%)+(+B20%)+C 10 9 (C+F20%+MK10%)+(+B20%)+C (C+F10%+MK10%)+(+B20%)+C (C+F20%+F10%+MK10%)+(+B20%)+C RTIFICIL EURL ETWORK rtificial neural network are nonlinear information (signal) processing devices, which are built from interconnected elementary processing devices called neurons. n artificial neural network () 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. n 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. 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

5 Dr. B.Vidivelli and. Jayaranjini. o Mix Cement(C ) (kg/m 3 ) Fly ash (F) (kg/m 3 ) ilica fume (F) (kg/m 3 ) Table.2 Proportion of mixes Metakao lin (MK) (kg/m 3 ) Fine aggregat e () (kg/m 3 ) Bottom ash (B) (kg/m 3 ) P (lit/m 3 ) lum p (mm) Experimental Compressive strength for 28 days(/mm 2 ) 1 CC C F F M KF B W C P Input layer Hidden layer f c Figure 1 The ystem used in -I model 306

6 Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks 1 11 C 2 12 F 3 13 F 4 14 M K 5 15 f c B Output layer W C 9 19 P Input layer 1.Hidden layer 2.Hidden layer Figure 2 The ystem used in -II model FEED FORWRD EURL ETWORK 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

7 Dr. B.Vidivelli and. Jayaranjini Table 3 The Input and Output quantities used in model. Datas Data used in training the models Minimum Maximum Input Variables ge of pecimen (day) Cement (Kg/m 3 ) ilica fume (Kg/m 3 ) Metakaolin (Kg/m 3 ) Fly ash (Kg/m 3 ) Bottom ash (Kg/m 3 ) and (Kg/m 3 ) Coarse ggregate (Kg/m 3 ) uperplasticizer (l/m 3 ) Output variable Compressive strength Table 4 Experimental results with Predicted results from models for 28 days Compressive strength (/mm 2 ) 28 days Mix Designation Experimental result -I -II % Error CC CC CC

8 Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks Table 5 Experimental results with Predicted results from models for 56 days Compressive strength (/mm 2 ) 56 days Mix Experimental Designation result -I -II % Error CC CC CC Table 6 Experimental results with Predicted results from models for 90 days Compressive strength (/mm 2 ) 90 days Mix Designation Experimental result -I -II % Error CC CC CC

9 Dr. B.Vidivelli and. Jayaranjini Table 7 Experimental results with Predicted results from models for 120 days Compressive strength (/mm 2 ) 120 days Mix Designation Experimental result -I -II % Error CC CC CC Two different multilayer artificial neural network architectures namely -I and -II were built. In training and testing of the -I and -II models constituted with two different architectures, C, F, F, MK, B,, C, W and P were input values, while f c value were used as output. In the -I & -II, 71 data of experimental results were used for training. In -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 -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 -I and -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 -I 310

10 Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks Figure 4 Experimental Results with training results of -I Figure 5 Experimental Results with training results of -I Figure 6 Experimental Results with training results of -I 311

11 -I -II -I -II -I -II -I -II Dr. B.Vidivelli and. Jayaranjini REULT D DICUIO In the training of -I and -II models, various experimental data are used. In the -I and -II models, 71 data of Experimental results were used for training. ll results obtained from experimental studies and predicted by using the training results of -I models for 28, 56, 90 and 120 days f c were given in fig.3, 4, 5 and 6 respectively. The linear least square fit line, its equation and the R 2 values were shown in these figures for the training data. lso, input values and Experimental results with training results obtained from -I and -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 quare (RM), Mean quare Error (ME), Mean bsolute Percentage Error (MPE) and R 2 training are given in table. While these values of RM, ME, MPE and R 2 from training in the -I model were found as 0.787, 0.619, 1.384% and 99.9% respectively. The best value of R 2 is 99.9% for training set in the -I model. The minimum value of R 2 is 99.6% for training set in the -I model. ll of the statistical value in table 9 show that the proposed -I and -II models are suitable and predict the 28, 56, 90 and 180 days compressive strength (f c ) values are very close to the experimental values. Table.8 The f c statistical values of proposed -I and -II models tatistical parameter (Training set) 28 days 56 days 90 days 120 days RME ME MPE (%) R COCLUIO In this tudy, 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 -I and -II. In two models developed on method, a multilayer feed forward neural network in a back propagation algorithm were used. In -I model, one hidden layer were selected. In the hidden layers 10 neurons were determined. In -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 -I & -II models were very close to the experimental results. Furthermore, according to the compressive strength results predicted by using -I and -II models, the results of -II model are closer to the experimental results. RME, ME, R 2 and MPE statistical values that are calculated for comparing experimental results with -I and -II model results have shown this situation. s a result, compressive strength values of 312

12 Prediction of Compressive trength of High Performance Concrete Containing Industrial by products Using rtificial eural etworks 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. can be suggested to predict the concrete compressive strength with high accuracy. REFERECE [1] oorzaei J, Hakim, Jaafar M, Thanoon W. Development of rtificial eural etworks For predicting Concrete Compressive trength. International Journal of Engineering and Technology. 2007, Vol. 4, [2] tici C, Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network Engineering Faculty, igde University, igde 51245, Turkey. Expert ystems with pplications. 08/2011; 38(8): [3] erkan ubas, Prediction of mechanical properties of cement containing class C fly ash by using artificial neural network and regression technique, cientific Research and Essay Vol. 4 (4) pp , pril, [4] eyed Jamalaldin eyed Hakim, Jamaloddin oorzaei, M.. Jaafar, Mohammed Jameel and Mohammad Mohammadhassani pplication of artificial neural networks to predict compressive strength of high strength concrete Vol. 6(5), pp [5] Vijay Pal ingh, Yogesh Chandra Kotiyal, Prediction of Compressive trength Using rtificial eural etwork International Journal of Civil, Environmental, tructural, Construction and rchitectural Engineering Vol:7, o:12, 2013 [6] akshi Gupta Using rtificial eural etwork to Predict the Compressive trength of Concrete containing ano-silica Civil Engineering and rchitecture 1(3): , 2013 [7] Wankhade M W and Kambekar, Prediction of Compressive trength of Concrete using rtificial eural etwork IJRR 2013, 2(2), [8] bdullah.m, l-mattarneh.h.m., Mohammed B.. tatistical of Lightweight Concrete mixtures, European journal of scientific re-search, Vol. 31,pp , [9] Padmanaban.I., Kandasamy. and atesan.c, tatistical of High and Low Volume of Fly sh High Compressive trength Concrete Inter-national Journal of pplied Engineering Research, Vol 4, pp , [10] Raghu Prasad BK, Eskandari H, Venkatarama Reddy BV. Prediction of compressive strength of CC and HPC with high volume fly ash using, Constr Build Mater 23; 2009: [11] Marai M. lshihri, hmed M. zmy, Mousa. El-Bisy, eural networks for predicting compressive strength of structural light weight concrete, Constr Build Mater 2009; 23: [12] Parichatprecha R, imityongskul P. nalysis of durability of high performance concrete using artificial neural networks, Constr Build Mater 2009; 23: [13] I 8112:2013. Indian standard Ordinary Portland Cement, 43grade pecification (econd Revision). [14] I: pecifications for Coarse and Fine ggregates from atural ources for Concrete. Bureau of Indian tandards, ew Delhi. [15] I dmixtures Indian tandard concrete admixtures specification [16] I: Methods of test for strength of concrete. ew Delhi; Bureau of Indian tandards

13 Dr. B.Vidivelli and. Jayaranjini [17] I: 10262:2009, Recommended Guidelines for Concrete Mix Design Indian tandard Institution, ew Delhi. [18] I: Plain and Reinforced Concrete. Code of Practice. Bureau of Indian tandards, ew Delhi. [19] Mustafa saridemir, Prediction of compressive strength of concrete containing metakaolin and silica fume by artificial neural networks, Constr Build Mater 2009; 40: [20] Dr aman H. l-hamawandi Dr bdul-ilah Y. Mohammed and Dr Rafa H.l- uhaili. Watershed Modeling Using rtificial eural etworks, International Journal of Civil Engineering and Technology, 6(4), 2015, pp [21] antosh Patil and hriniwas Valunjkar. Forecasting of Daily Runoff Using rtificial eural etworks, International Journal of Civil Engineering and Technology, 5(1), 2014, pp [22] P.J.Patel, Mukesh. Patel and Dr. H.. Patel. Effect of Coarse ggregate Characteristics on trength Properties of High Performance Concrete Using Mineral and Chemical dmixtures, International Journal of Civil Engineering and Technology, 4(2), 2013, pp [23] Topçu IB, arıdemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic, Comp Mater ci 2008; 41(3):