基于改进微粒群法和有限元法的混凝土温控方案优化  

Optimization of concrete temperature control measures based on improved particle swarm optimization and finite element method

作　　者：强晟[1] 郑伟忠[1,2] 张勇强[1,2] 刘连建[3] 

机构地区：[1]河海大学水利水电学院,南京210098 [2]广州市水务局,广州510640 [3]淮安市水利局,淮安223005

出　　处：《农业工程学报》2014年第16期75-83,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51109071);江苏省淮安市水利院士工作站项目

摘　　要：对于大体积混凝土最优温控方案的选取,传统方法是按照规范的要求和人工反复修改方案,存在效率低下和受限于经验的问题。该文采用改进的微粒群算法(PSO,particle swarm optimization)优化方法,以及基于有限元(FEM,finite element method)的混凝土温度场和应力场仿真算法联合进行优化。算例设定了2个优化目标,即只考虑安全性的单目标优化(多特征点达到最小防裂安全系数1.8),以及考虑安全性和经济性的双目标优化(温控综合成本最小化)。计算结果表明,所提方法能够实现温控方案的自动寻优,优化结果更科学合理,总体研究效率可提高50%以上。考虑双目标优化后,在确保防裂安全的条件下能够明显降低温控措施的综合成本。For the selection of temperature control measures for massive concrete, traditional methods are fully in accordance with the industry standard requirements and subject to repeated artificial amending by practical experience in engineering design and construction. Therefore, it is inefficient and limited by the designer＆#39;s experience. In this paper, an improved particle swarm optimization （PSO） combined with concrete temperature field and stress field based on the finite element method （FEM） was tested to select the optimal concrete temperature control measures. In the simulation cases, two optimization objectives were defined. The first objective was that the tensile stress of multi feature points should satisfy a safety-cracking factor of at least 1.80. The second was that the whole temperature measures cost should be minimal. The optimization computation was implemented separately with only a single objective for safety factor and both objectives for safety and cost. From the results of 6 calculation cases for a fictitious small-scale concrete dam structure, the following conclusions were drawn. 1） The results show that the proposed method can achieve automatic finding of the temperature control measures optimization, and the optimization results are more scientific and more reasonable. If the ranges of the temperature control parameters can be defined reasonably, the dependence of optimization on humans can be decreased. It will increase the scientificity and persuasion of the temperature control scheme, especially under the complicated situation of more optimization objects, which is very difficult to draw a most reasonable quantitative measures composition. 2） The efficiency of the whole research is improved noticeably. According to experience, if the safety factor is taken as the only objective, the optimization will cost 5 to 7 days by a medium level researcher. In this paper, the time cost of the intelligent optimization is only 2.2 days. 3） After considering the two-objective optimizat

关 键 词：混凝土 有限元法 温控 微粒群算法 仿真计算 

分 类 号：TV331[水利工程—水工结构工程]