江淮水利科技Issue(5)：9-15,7.DOI:10.20011/j.cnki.JHWR.202505003
基于HHO-BP神经网络的混凝土坝温控措施智能优选
Intelligent optimization of temperature control measures for concrete dams based on HHO-BP neural network
摘要
Abstract
During the high-temperature construction season of concrete dams,traditional BP neural networks or optimization al-gorithms such as PSO and GA often exhibited poor local search performance and slow convergence when selecting optimal tem-perature control measures.Based on the concrete temperature control standards for the Shanqianli dam under construction,this study established an intelligent optimization model using an HHO-BP neural network.Learning samples obtained through or-thogonal design and temperature field simulation were used to train the model,which was then employed to determine the opti-mal casting temperature,cooling water temperature,and cooling water flow rate for the dam from May to August.The final fit-ness values of the HHO-BP neural network model were all below 0.4,with only 31 iterations required.The temperature control measures optimized by the HHO-BP neural network model yielded maximum temperatures and maximum cooling rates for dif-ferent months during the high-temperature season that were close to the temperature control standards specified in Standard for construction of mass concrete.The average monthly deviations were only 0.13℃ and 0.073℃/day,respectively.The results in-dicated that using the HHO-BP neural network model for optimizing temperature control measures achieved fast convergence and excellent performance.关键词
混凝土坝/温控措施/智能优选/温度场分析/HHO算法Key words
concrete dams/temperature control measures/intelligent optimization/temperature field analysis/HHO algorithm分类
建筑与水利引用本文复制引用
苏敏,王祥,王帅,章伟,程永亮,向紫怡..基于HHO-BP神经网络的混凝土坝温控措施智能优选[J].江淮水利科技,2025,(5):9-15,7.基金项目
国家自然科学基金项目(52239009) （52239009）
