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基于遗传算法的粉煤灰高性能混凝土配合比优化设计
引用本文:刘翠兰,李高峰,乔红霞,彭建,苏学龙.基于遗传算法的粉煤灰高性能混凝土配合比优化设计[J].兰州理工大学学报,2006,32(5):133-135.
作者姓名:刘翠兰  李高峰  乔红霞  彭建  苏学龙
作者单位:1. 兰州理工大学,土木工程学院,甘肃,兰州,730050
2. 兰州市房地产开发公司,甘肃,兰州,730030
3. 白银市大地城建开发有限公司,甘肃,白银,730090
基金项目:甘肃省建设科技攻关项目(JK2002-3)
摘    要:采用遗传算法对粉煤灰高性能混凝土进行配合比优化设计,以经济成本为目标函数、以满足粉煤灰高性能混凝土各项性能指标为约束条件建立适应遗传算法的数学模型,用二进制编码表示个体的染色体位串,通过选择、交叉、变异等遗传算子作用进行全局性概率搜索,以优胜劣汰为原则,最终达到最优的群体和个体.将优化结果和工程实例的配合比进行对比,可见该方法可降低造价、节约能源,具有较好的工程使用价值.

关 键 词:粉煤灰高性能混凝土  配合比优化设计  遗传算法  造价
文章编号:1673-5196(2006)05-0133-03
收稿时间:2005-08-30
修稿时间:2005年8月30日

Optimization design of ingredient of high-performance fly ash concrete based on the genetic algorithm
LIU Cui-lan,LI Gao-feng,QIAO Hong-xia,PENG Jian,SU Xue-long.Optimization design of ingredient of high-performance fly ash concrete based on the genetic algorithm[J].Journal of Lanzhou University of Technology,2006,32(5):133-135.
Authors:LIU Cui-lan  LI Gao-feng  QIAO Hong-xia  PENG Jian  SU Xue-long
Institution:1. College of Civil Engineering, Lanzhou Univ. of Teen , Lanzhou 730050, China; 2. Lanzhou Real Estate Development Corp. , Lanzhou 730030, China; 3. Baiyin Dadi Urban Construction Development Co. , LTD, Baiyin 730090, China
Abstract:Genetic algorithm was probed for optimization design of the ingredient of high-performance fly-ash concrete.A mathematical model was set up to meet the needs of genetic algorithm,where economical cost was taken as object function and several performance indexes of the concrete to be designed were taken as the constrain condition.During global probability searching,and based on the rule of survival of the fittest,the optimal group and individual were finally obtained by using the binary coding for individual chromosome bit string and arithmetic operators such as selection,cross,and mutation.It could be seen by comparing the optimization results with the engineering case,that the genetic algorithm could decrease the cost and economize the energy consumption,having a good application in engineering.
Keywords:high-performance fly-ash concrete ingredient  optimization mix design  genetic algorithm  cost
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