Predicting the Compressive Strength of Desert Sand Concrete Using ANN: PSO and Its Application in Tunnel

Abstract

Desert sand is one of the current research hotspots in alternative materials for concrete aggregates. In the process of practical application, compressive strength is an essential prerequisite for studying other properties. Based on the current research situation, a prediction technology of compressive strength of desert sand concrete (DSC) is proposed based on an artificial neural network (ANN) and a particle swarm optimization (PSO). The technology is a prediction model that adjusts the network architecture by using the PSO method based on the ANN optimization model. Water-binder ratio, sand ratio, replacement rate of desert sand, desert sand type, fly ash content, silica fume content, air content, and slump were selected as the neural network’s inputs. The compressive strength data of 118 different combinations of influencing variables were tested to establish the dataset. The results show that the PSO method is efficient for the ANN in DSC compressive strength research. Furthermore, referring to this method, DSC is applied to the shotcrete of tunnels in construction engineering successfully, and the dust particle content during construction is evaluated.