Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks

Abstract This paper presents properties of high performance composite cementitious systems. The properties investigated were compressive strength, tensile strength, gas permeability and rapid chloride ion penetration of concrete incorporating composite cementitious materials as partial cement replacement prepared with various water-binder ratios. There is an interaction of PFA and SF with the level of replacement. The incorporation of 8 to 12% SF as cement replacement yielded the optimum strength, permeability and chloride ion penetration values. Based on the experimentally obtained results, the applicability of artificial neural network for the prediction of compressive strength, tensile strength, gas permeability and chloride ion penetration has been established. The predicted values obtained using artificial neural networks have a good correlation between the experimentally obtained values. Therefore, it is possible to predict strength and permeability of high performance concrete using artificial neural networks.

[1]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[2]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[3]  Manish A. Kewalramani,et al.  Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks , 2006 .

[4]  Rafat Siddique,et al.  Supplementary Cementing Materials , 2011 .

[5]  J. G. Cabrera,et al.  A new gas permeameter for measuring the permeability of mortar and concrete , 1988 .

[6]  F. Demir.  Prediction of elastic modulus of normal and high strength concrete by artificial neural networks , 2008 .

[7]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[8]  Odd E. Gjørv,et al.  Effect of silica fume on pore structure and chloride diffusivity of low parosity cement pastes , 1991 .

[9]  A. Neville,et al.  High-performance concrete demystified , 1993 .

[10]  Mohammad Iqbal Khan,et al.  Nanostructure and Microstructure of Cement Concrete Incorporating Multicementitious Composites , 2010 .

[11]  Hui Li,et al.  Artificial neural network analysis of concrete carbonation under sustained loads , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[12]  Mohammad Iqbal Khan,et al.  Permeation of High Performance Concrete , 2003 .

[13]  Cyril J. Lynsdale,et al.  Porosity and strength of PFA/SF/OPC ternary blended paste , 2000 .

[14]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .