An intelligent hybrid system for predicting the tortuosity of the pore system of fly ash concrete

•A new approach to predict the tortuosity of the pore system of fly ash concrete.•Implementation of a hybrid system combining neural networks with genetic algorithms.•Numerical investigation effectiveness of the key mechanical and transport properties.•Optimum threshold estimation for tortuosity based on strength-porosity correlation. In this paper, the soft computing approach is proposed to predict the transport tortuosity of the pore system of fly ash concrete as an important macroscopic parameter by building an intelligent hybrid system. This system is designed with a modular architecture composed of three neural network models optimized by genetic algorithms. The system is primarily based on the two main properties, compressive strength and porosity, which depend mainly on the concrete mix design, fly ash content and age. Performance of the proposed hybrid system is validated experimentally. Results indicate that concrete mixtures with 30% fly ash show the optimum transport tortuosity of the pore system. Moreover, results indicate also a threshold value of 33 for the transport tortuosity, above which both compressive strength and porosity are unimproved. This study concludes that the proposed system provides basis related to concrete durability and performs very well using a user-friendly interface to promote its effective utilisation.