Improved BP Neural Network for Predicting the Bearing Capacity of Concrete Components
Due to various factors,the bearing capacity of large-diameter concrete flexural members will change during use.To address this issue,this article put forward a method for predicting the bearing capacity of large-diame-ter concrete flexural member.Firstly,five major factors affecting the bearing capacity of large-diameter concrete flex-ural member were identified.Then,these factors were served as inputs to construct a BP neural network model for pre-dicting the bearing capacity of large-diameter concrete flexural members.Finally,the parameters of the BP neural network model were optimized by the simulated annealing-particle swarm algorithm,and then the optimized model was used to complete the prediction of the bearing capacity of large-diameter concrete flexural members.The experimental results show that the proposed method has higher prediction accuracy and efficiency for the bearing capacity of large-diameter concrete flexural member and better overall application effect.
Large diameter concretePrediction of bearing capacityFlexural memberBP neural network mod-elSimulated annealing-particle swarm optimization algorithm
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