基于改进骨干粒子群算法的大体积混凝土水化热管冷参数优化

Optimization of Cooling Parameters of Large-Volume Concrete Hydration Heat Pipes Based on Improved Backbone Particle Swarm Algorithm

针对大体积混凝土浇筑时的水化热温度控制问题,提出了一种基于动态惯性权重改进骨干粒子群算法的管冷参数优化方法。以某深水库区大跨度拱桥拱座大体积混凝土为工程背景,通过有限元分析软件二次开发子程序进行建模分析与优化计算,求解该管冷布置方案下的最佳管冷参数。结果表明:有限元分析软件二次开发子程序可以较为准确地计算分层浇筑下大体积混凝土的温度变化规律;基于动态惯性权重改进的骨干粒子群算法相较于标准粒子群算法在该优化问题上具有更好的收敛性;相较于设计管冷参数,优化后的管冷参数可以明显提高降温效果,第一层混凝土核心区温度最高降幅5.8℃,第二层混凝土核心区温度最高降幅8.9℃。

Aiming at the problem of hydration heat temperature control during large-volume concrete pouring,a tube cooling parameter optimization method based on dynamic inertia weights to improve the backbone particle swarm algorithm is proposed.The large-volume concrete of the arch of a long-span arch bridge in a deep reservoir area is used as the engineering background,and the modeling analysis and optimization calculation are carried out through the secondary development subprogram of finite element analysis software to solve the optimal tube cooling parameters under the tube cooling layout scheme.The calculation results show that the secondary development subprogram of finite element analysis software can accurately calculate the temperature change law of large-volume concrete under layered pouring.Compared with the standard particle swarm algorithm,the improved backbone particle swarm algorithm based on dynamic inertia weights has better convergence in this optimization problem.Compared with the designed tube cooling parameters,the optimized tube cooling parameters can improve the cooling effect more obviously,with the maximum temperature reduction of 5.8°C in the core area of the first layer of concrete and 8.9°C in the core area of the second layer of concrete.