Predictionof Compressive Strength of Concrete usingaritificial Neural Network: A case study

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1 Predictionof Compressive Strength of Concrete usingaritificial Neural Network: A case study Dr. Pradeep K. Goyal 1 and Rohit Prajapati 2 1 Associate Professor& Head, Government Engineering College, Ajmer, Rajasthan 2 Practicing Engieer,New Delhi ABSTRACT The computation of strength of concrete and improvement in its properties, have always been interesting areas of research. The improvement in strength and evolution of desired properties have increased the use of concrete in many areas of construction but it also has some limitations which need careful supervision and maintenance for better results. Artificial neural networks are very much similar to human nerves system. In this paper,compressive strength for three types of mix designs namely, M15, M20 and M25 is predictedusing artificial neural network. The data is collected during the construction of main dam of Rajghar Medium Irrigation Project located at Bhiwani Mandi in Jhalawar district of Rajasthan.The experiment has shown good results, artificial neural network can be used to predict compressive strength of concrete. 1. INTRODUCTION In this developing era, the advancements in science and technology have increased the participation of different fields of science in development of new technologies and methods, in order to increase the efficiency, performance and precise output. The development of new technology requires solution of many complex problems of engineering and mathematics. The solutions of such problems are time consuming and require more attention and accuracy. There is a need of introducing new methods which are capable of providing accurate and refined results in less time. Now a day, many software developers are offering advance software for the computation of complex problems. A variety of modeling, designing and computing problems of engineering fields can be solved very easily through the software. As well as in the area of civil engineering, software are available for designing, drafting and calculating various problems for complicated structures. Project planning, scheduling and management of material and manpower have become so simple with such tools. The computation and improvement in strength of concrete has always been an interesting area of research. The improvements in strength and evolution of desired properties have increased the use of concrete in many areas of construction. But it also has some limitation which needs careful supervision and maintenance till it gains sufficient strength [1]. 2. Compressive Strength Of Concrete Concrete is a well known building material widely accepted over the world for many types of construction works like buildings, dams, highways, bridges etc., due to its ability to shape into any desired shape. Concrete has two major parts binder and inert material, in a specific ratio to obtain desired strength. Binder is typically cement, which may have different grade which depends upon mixture of various binding material like lime, silica, fly ash etc. Concrete get hardened because of chemical reaction taken place between cement and water, it gets stronger with the passage of time [2]. Properties like durability, strength, permeability etc. of concrete depends upon its constituents and procedure of mixing, placing, curing and compaction during casting. A variety of properties can be induced to a desirable level by simply controlling the ratio of ingredients during the mixing process [3]. The setting and hardening properties of concrete depends upon various factors. The compressive strength of concrete is measured at several intervals to ensure that it gains enough strength. The 28 day compressive strength of concrete specimen is considered as standard strength parameter. But for many of the projects it is not practically possible to wait for 28 days, as the speed of construction is to be maintained due to time and cost related factors. The compressive strength of concrete for different mix designs can be predicted by using artificial intelligence neural network which is based upon the results of previous experiments. 276

2 3. Artificial Neural Networks Artificial Neural Networks are very much similar to human nerves system. Neural network consist of numbers of neurons, these neurons are same as the nerve cells of human beings, just like nerves system process the information fed by senses and gives output. ANN are widely accepted and studied in many fields to get performance and output like human beings, which is based on pattern recognition and system identification. The output of our nerves system is based on previous experience and induced inelegancy through learning, the same phenomenon is applicable for ANN system. An ANN system is taught by pattern recognition and different learning processes. ANN can be designed for a specific problem which gives output by adjusting and processing data. There is no need to understand the internal working procedure; we can directly feed input data and target data for pattern recognition [4]. Fig.1: Neural Netwo rk Structure for prediction of compressive strength 4. Experimentation And Data Collection Data collection and their authenticity is the most important part of this project. This is the main key of this ANN model. Basically in ANN models we need to train a system which is typically made of number of neurons. These neurons give output on the basis of which is being taught to them by feeding a particular set of data. A sufficient number of data set have to be collected by observing each testing, the more the number of tests the more the authentic results can be obtained. The advantage of performing numbers of tests is, variations in results can be known for a particular mix design, thus maximum and minimum values for a mix is known. Each and every test results should be carefully observed and recorded. Every set of mix design data contain many influencing vectors corresponding to compressive strength. The data collected from construction site of main dam of Rajghar Medium Irrigation Project located at BhiwaniMandi in Jhalawar district of Rajasthan. Three type of mix designs M15, M20 and M25were being used in between March 2016 and April 2016.For each mix 60 cubes were tested, 30 cubes for 7 day strength and the same for 28 day strength. The range of input vectors for M15, M20 and M25 are shown is Table. 1. Details Table 1.Range of Quantities (kg/ M 3 )of input vectors Range of Quantities (kg/ M 3 ) # (Liters)* Cement # Flyash # Sand I (Natural) # Sand II(Crushed) # Aggregate(5-20 mm) # Aggregate(20-40mm) # Water * 277

3 5. Data Preparation During the preparation of data, problem was to deal with the wide range of input data. The quantity of cement was 221 kg/ M 3 in M15, 272kg/M 3 in M20and 306 kg/m 3 in M25, similarly the same variation is also found for the other ingredients of concrete. Thus the input data are varying is a very long range which can influence the accuracy of output data.to overcome from this problem normalization techniques are used, which converts the data between a range of 0 to 1, corresponding to their weightage. 6. Training of Data Using ANN A three step schematicmethodology is shown in figure 1 for the prediction of compressive strength of concrete using MATLAB. Fig.2. Process of Prediction of Compressive strength using Neural Network Step 1 : The experimental results arebeing used for training and testing of neural network model; input and target values are prepared which is explained above. Step 2 : The data were randomly selected in proportion of 70%, 15 % and 15 % for training, validation and testing respectively, gives output after training the neural network. Step 3 : The predicted data is than analyzed for the assurance weather the prediction by network is acceptable or not. 7. Analysis of Results The following tables 2 and 3 shows the difference between target and output given by Neural Network. The relative errors for this model do not increase over a level nearly about 10 % for the most of the results. Table 2Testing results for 7 days compressive strength S No. Details Target output (M Pa) NN output (M Pa) Error (M Pa) Relative error (%) 1 M M M M M M M M M M M M M M M

4 Table 3 Testing results for 28 days compressive strength S No. Details Target output (M Pa) NN output (M Pa) Error (M Pa) Relative error (%) 1 M M M M M M M M M M M M M M M Correlation coefficients have values between +1 and -1. A correlation coefficient of +1 indicates perfect positive correlation and coefficient of -1 indicates a perfect negative correlation. The correlation coefficient (R) for training, testing, validation and overall data is illustrated in figure 3. The total value of R for training, validation and test is , which is satisfactory by the point of view of a civil engineer as already discussed that concrete is a highly complex material. 279 Fig.3:Coefficient of correlation for training, validation, test and all data set

5 8. Conclusion This study represents an application of neural network model to predict the compressive strength of concrete. Using neural networks, the prediction of expected strength at constructional sites is a quite difficult task because concrete is a complex material. The development of strength of concrete with time depends upon many factors like temperature, quality of materials used, curing, environmental conditions etc. which can influence the output on site. But the results demonstrate good possibilities of the use of Neural Network modeling in prediction of compressive strength of concrete, because the relative errors were of small magnitudes. A positive correlation was obtained having value nearly about 0.80 which is considered fairly well.if a larger amount of data is fed to the model the errors can be reduced, because the neural networks are trained through the input data, and variety of test results will help in learning processes. The neural networks were trained using the concrete mix proportions of three concrete grades with test results for 7days and 28 days compressive strength data. The compressive strength of concrete is predicted by the trained neural networks. The neural network modeling can contribute in predicting the strength for other concrete mixes. A more accurate neural network model can be constructed by collecting data for various mix proportions for a particular region, because the environmental conditions (like temperature, relative humidity etc.), quality and specification of raw materials and workmanship methods are usually same for a region. Through a database collection of different mix proportions, one can predict strength for a particular mix in future without waiting for test results for seven or twenty eight days, if it is already used in past and tested in neural network. References [1] Rohit Prajapati Prediction of compressive strength of concrete using Artificial Neural Network M. Tech Thesis, Institute of Engineering and Technology, Bhagwant University Ajmer, India, May [2] Santhakumar A. R., Concrete Technology Published by Oxford University Press, [3] Gambhir M. L., Concrete Technology Published by Tata McGraw-Hill Publishing Company Ltd. New Delhi, [4] Neural Network ToolboxFor Use with MATLAB, Howard DemuthMarkBealeUser s Guide. [5] Simon Haykin. Neural Networks A Comprehensive Foundation, Prentice-Hall, [6] E. Rasa, H. Ketabchi and M.H. Afshar, Predicting Density and Compressive Strengthof Concrete Cement Paste Containing Silica Fume Using Artificial Neural Networks, [7] Jong-In Kim, Doo Kie Kim, Maria Q. Feng, and Frank Yazdani, Application of Neural Networks for Estimation of Concrete Strength Vol. 16, No. 3, pp ,May/June [8] Discussion of Application of NeuralNetworks for Estimation of ConcreteStrength by Jong-In Kim, Doo KieKim,Maria Q. Feng, and Frank Yazdani, Ashu Jain; SudhirMisra; and Sanjeev Kumar Jha, [9] B.K. Raghu Prasad, Hamid Eskandari and B.V. Venkatarama Reddy, Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN Construction and Building Materials 23, , [10] Vahid. K. Alilou& Mohammad. Teshnehlab Prediction of 28-day compressive strength of concrete on the third day using artificial neural networks. [11] [12] Ni, H. G., and Wang, J. Z., Prediction of compressive strength of concrete by neural networks. Cem. Concr. Res., 30_8_, , [13] Nehdi, M., jebbar, Y.D. and Khan, A. \Neural networkmodel for cellular concrete", ACI Materials Journal,98(5), pp , [14] Hung, S.-L. and Jan, J.C., "MS CMAC neural network learning model in structural engineering Journal of Computing in Civil Engineering, ASCE, Vol. 13, No. 1, pp. 1-11,