基于DPO-BP的建筑混凝土配合比优化设计研究  

Research on Optimal Design of Building Concrete Mixture Based on DPO-BP

作　　者：陈艳 Chen Yan(Fuzhou Software College,Fuzhou 350004,China)

机构地区：[1]福州软件职业技术学院,福州350004

出　　处：《大理大学学报》2021年第12期36-39,共4页Journal of Dali University

摘　　要：为解决BP神经网络在混凝土抗压强度的预测中训练效果差、泛化能力低的缺陷,将海豚算法(DPO)与BP神经网络结合构建基于DPO优化BP神经网络模型(DPO-BP)应用于混凝土配合比优化,提出了一套切实可行的混凝土配合比设计方案。结果表明,与BP算法、遗传算法优化BP神经网络(GA-BP)、粒子群算法优化BP神经网络(PSO-BP)等算法相比,该优化方式具有收敛速度快、鲁棒性好等优点。DPO-BP算法求解精度高、可移植性强,还可以用作边坡稳定性判断、机器故障诊断、空气水体质量评价等诸多领域,具有重要的工程价值。In order to solve the defects of poor training effects and low generalization ability of neural network in the prediction of concrete compressive strength,in this paper,the dolphin algorithm(DPO)and BP neural network are combined to construct a BP neural network model based on DPO(DPO-BP)and applied to optimize the concrete mixing ratio,and a set of feasible concrete mixing-ratio design schemes are proposed.The results show that compared with algorithms,genetic optimization BP neural network(GA-BP)and particle swarm optimization BP neural network(PSO-BP),this optimization method has the advantages of fast convergence and good robustness.The DPO-BP algorithm has high solution accuracy and strong portability.It can also be used for slope stability judgment,machine fault diagnosis,air and water quality evaluation and many other fields.It has very significant engineering value.

关 键 词：海豚算法 BP神经网络 混凝土 抗压强度 配合比 

分 类 号：TU74[建筑科学—建筑技术科学]