摘要
闸墩混凝土温控防裂是一个与温控措施和材料参数相关的复杂多因素系统优选问题,本文尝试已知混凝土热力学材料参数情况下的温控措施优选,将闸墩混凝土结构内部和表面主拉应力历时曲线和抗拉强度增长曲线关系的最小值作为输入,闸墩表面保温效果、浇筑温度、通水水温、通水时间作为输出,建立了温控措施优选的神经网络模型,采用均匀设计原理进行温控参数组合,并采用水管冷却有限元法仿真分析含冷却水管的闸墩混凝土结构温度场和徐变应力场,获得样本进行学习,以此训练好的网络描述结构主拉应力历时曲线和抗拉强度增长曲线关系的最小值与不同温控措施的非线性关系。将合适的结构主拉应力历时曲线和抗拉强度增长曲线关系的最小值输入训练好的网络,可自动优选出温控防裂措施。算例分析表明,本文建立的温控措施优选神经网络模型是可行的。
To the sluice pier concrete,temperature control and crack prevention is a complex and multi-factor problem of system optimization related to temperature control measures and material parameters. This paper tries optimal temperature control measures with the known con-crete material thermodynamics parameters,takes minimum values of the relationships between the sluice pier concrete structure’s internal/surface principal tensile stress duration curve and tensile strength growth curve as inputs and the sluice pier superficial heat preservation effect, pouring temperature,pipe cooling temperature and duration time as outputs,establishes the neural network model of the optimal temperature control measures,takes the uniform design principle to have the temperature control parameter combination,adopts the pipe cooling finite element method（FEM）to simulate and analyze the temperature field and creep stress field of the sluice pier concrete structure with cooling pipe,and gets samples to train network to de-scribe the nonlinear relationship between the relationship’s minimum value of the structure’s principal tensile stress duration curve and tensile strength growth curve and different tempera-ture control measures. Inputting the appropriate relationship’s minimum value of structure’s the principal tensile stress duration curve and tensile strength growth curve to the trained net-work,it can automatically select the optimal temperature control measures. The example shows that the neural network model of the optimal temperature control measures is feasible.
出处
《中国工程科学》
北大核心
2014年第3期59-63,共5页
Strategic Study of CAE
基金
国家自然科学基金项目(51209124)
关键词
闸墩
表面保温
通水冷却
浇筑温度
智能优选
sluice pier
superficial heat preservation
water cooling
pouring temperature
intelligent optimization
