An Optimization Method for Mix Proportion of Wet-Mix Shotcrete: Combining Artificial Neural Network with Particle Swarm Optimization

Abstract

Wet-mix shotcrete has been widely used to support roadways during the excavation of underground mining. In practice, the mix proportion of wet-mix shotcrete plays a key role in later successful application. To obtain an optimal mix proportion, a large number of trial physical experiments should be carried out. Therefore, in this paper, a new ANN–PSO model is proposed to obtain the mix proportion of wet-mix shotcrete quickly, precisely and economically. The artificial neural network (ANN) model was used to establish the objective functions for particle swarm optimization (PSO) optimization, while the PSO was adopted to optimize mix proportions of wet-mix shotcrete to achieve optimal objectives. This hybrid model was applied to optimize mix proportions of wet-mix shotcrete in the Jinchuan mine. The results revealed that the ANN model yielded a mean relative error (MRE) of 2.755% and an R2 of 0.980, indicating an excellent prediction to establish the reasonable objective function. Additionally, PSO spent less than 60 s obtaining an optimal mix proportion of wet-mix shotcrete required by the mine. Consequently, this ANN–PSO model can be used as an efficient design guide to facilitate decision making, prior to the construction phase.