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