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

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.

► PFA and/or SF were incorporated as partial cement replacement to produce HPC.
► Results of strength, gas and chloride permeability of concrete mixes are reported.
► Models presented provide reasonable predictions of properties under investigation.
► ANN was used for the prediction of properties under investigation.