基于MATLAB和ANSYS的大体积混凝土水化热效应分析

Hydration Heat Effect Analysis of Massive Concrete Based on MATLAB and ANSYS

大体积混凝土水化热效应产生的温度场和应力场十分复杂,在施工过程中经常会因为内外温度差异产生有害裂缝。在有限元仿真过程中,选取的基于《大体积混凝土施工规范》(GB50496-2009)中规定的热学参数只适用于强度等级为C25~C40的低强度混凝土。对于大跨径桥梁中常用的高强度大体积混凝土,热学分析参数的选取是一个难点。为了解决这一问题,文章基于遗传算法,先确定温度场分析时的反演热学参数,建立目标适应度函数,然后针对ANSYS二次开发建立温度场反分析模型,最后研究在MATLAB中使用遗传算法工具箱调用ANSYS中保存的温度场数据,对比现场实测温度数据,反演得到混凝土的最大绝热升温系数、导温参数、导热系数和表面放热系数。用该方法对某高架桥大体积混凝土承台水化热效应进行反馈分析,结果表明,基于改进遗传算法反演参数有限元模型得到的温度场计算值与实测值误差在10%以下,结果吻合良好。

The temperature field and stress field caused by the hydration heat effect of mass concrete are very complicated. In the process of construction, harmful cracks often occur due to the difference of internal and external temperature. In the process of finite element simulation, the selected thermal parameters based on ＂Mass Concrete Construction Specifications＂ （GB50496-2009） are only applicable to low-strength concrete with strength grade C25 ～ C40. The selection of thermal analysis parameters is a challenge for the commonly used high-strength mass concrete in long-span bridges. In order to solve this problem, based on the genetic algorithm, the thermal parameters in the analysis of the temperature field are firstly determined and the target fitness function is es- tablished. Then the inverse analysis model of the temperature field is established for the secondary development of ANSYS. Finally, the genetic algorithm toolbox is used to call the temperature field data saved in ANSYS to compare with the measured temperature data in the field, then the maximum adiabatic temperature rise, thermal conductivity coefficient, thermal conductivity and surface heat dissipation coefficient of the concrete are obtained by inversion. This method is used to feedback analysis of the hydration heat effect of mass concrete slab of a viaduct. The results show that the error between the calculated value based on the improved genetic algorithm inversion parameter finite element model and the measured value of the temperature field is below 10%. The results are in good agreement.