Abstract
The aim of this study was to develop a nature-inspired metaheuristic method to predict the creep strain of green concrete containing ground granulated blast furnace slag (GGBFS) using an artificial neural network (ANN)model. The firefly algorithm (FA) was used to optimize the weights in the ANN. For this purpose, the cement content, GGBFS content, water-to-binder ratio, fine aggregate content, coarse aggregate content, slump, the compaction factor of concrete and the age after loading were used as the input parameters, and in turn, the creep strain (εcr) of the GGBFS concrete was considered as the output parameters. To evaluate the accuracy of the FA-ANN model, it was compared with the well-known genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). Results indicated that the ANNs model, in which the weights were optimized by the FA, were more capable, flexible and precise than other optimization algorithms in predicting the εcr of GGBFS concrete.
Highlights
The time-dependent deformation of concrete, as a result of creep strains, severely affects the durability of concrete structures
The results presented indicated that the artificial neural network (ANN) model optimized by the firefly algorithm (FA) determined the creep strain values more accurately than other algorithms
It is possible to predict the creep strain of green concrete with ground granulated blast furnace slag (GGBFS) using artificial neural networks (ANN) and the nature-inspired metaheuristic firefly algorithm (FA)
Summary
The time-dependent deformation of concrete, as a result of creep strains, severely affects the durability of concrete structures. As stated by El-Shafie and Aminah [1], the stochastic nature of creep deformation and its reliance on a large number of uncontrolled parameters (e.g., relative humidity, time of load application, stress level) makes the process of the prediction and development of accurate mathematical models very difficult (almost impossible). The creep strain (εcr ) depends primarily on the composition of the concrete This composition has recently been more frequently modified using eco-friendly admixtures. The concrete obtained applying these kinds of admixtures are usually known as “green concrete”. One example of these admixtures is ground granulated blast furnace slag (GGBFS).
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