改进BP神经网络的混凝土构件承载力预测仿真
Improved BP Neural Network for Predicting the Bearing Capacity of Concrete Components
夏运生 1白鑫 2马三蕊1
作者信息
- 1. 黄河交通学院,河南 焦作 454950
- 2. 宁波大学,浙江 宁波 563000
- 折叠
摘要
受到多种因素的影响,大直径混凝土受弯构件在使用期间其承载力将发生变化,为此提出大直径混凝土受弯构件承载力预测方法.确定影响大直径混凝土受弯构件承载力的五大因素,将影响因素作为输入建立用于大直径混凝土受弯构件承载力预测的BP神经网络模型,通过模拟退火-粒子群混合算法优化BP神经网络模型参数,并使用优化后BP神经网络模型完成大直径混凝土受弯构件承载力预测.实验结果表明,所提方法的大直径混凝土受弯构件承载力预测精度和效率更高,整体应用效果更好.
Abstract
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.
关键词
大直径混凝土/承载力预测/受弯构件/神经网络模型/模拟退火-粒子群混合优化算法Key words
Large diameter concrete/Prediction of bearing capacity/Flexural member/BP neural network mod-el/Simulated annealing-particle swarm optimization algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据