Soft computing based approaches for high performance concrete

Different parameters influence the compressive strength and workability properties of high performance concrete (HPC) mixes. Accordingly, an extensive understanding of relation between these parameters and properties of the resulting matrix is required for developing a standard mix design procedure for HPC mix. The complex behaviour of strength and workability improvement and a need to avoid trying several mix proportions to generate a successful mix suggest the necessity to develop comprehensive mathematical models to be able to evaluate the performance characteristics of HPC mixes with high accuracy. Therefore, in this paper, linear genetic programming (LGP) is utilised for the first time in the literature, to develop mathematical models to be able to predict the strength and slump flow of HPC mixes from the influencing parameters. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand cement ratio, coarse aggregate cement ratio, water cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models.