Created by W.Langdon from gp-bibliography.bib Revision:1.5433
To avoid testing several mix proportions to generate a successful mix and also simulating the behaviour of strength and workability improvement to an arbitrary degree of accuracy that often lead to savings in cost and time, it is idealistic to develop prediction models so that the performance characteristics of HPC mixes can be evaluated from the influencing parameters. Therefore, in this paper, linear genetic programming (LGP) is used for the first time in the literature to develop mathematical models to be able to predict the strength and slump flow of HPC mixes in terms of the variables responsible. 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.
The results of the present study, based on the values of performance measures for the models, demonstrated that for the prediction of compressive strength the optimum MLP model outperforms both the best team and the best single solution that have been created by LGP. It can be seen that for the slump flow the best LGP team solution has produced better results followed by the LGP best single solution and the MLP model. It can be concluded that LGPs are able to reach a prediction performance very close to or even better than the MLP model and as promising candidates can be used for solving such complex prediction problems.",
1College of Civil Engineering, Iran University of Science & Technology (IUST), Tehran, Iran 2College of Civil Engineering, Tafresh University, Iran 3Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland",
Genetic Programming entries for A H Alavi Ali Akbar Heshmati Hossein Salehzadeh A H Gandomi Amin Askarinejad