基于交叉全局人工蜂群算法的拱坝热学参数反演分析

Inverse Analysis of Arch Dam Thermal Parameters Based onCross-Global Artificial Bee Colony Algorithm

拱坝施工期温度受到气温、冷却通水、表面保温等因素影响,热学参数实际值与室内试验测值之间存在较大误差。通过分布式光纤测温技术获取的温度数据和人工蜂群智能优化算法对混凝土热学参数进行识别,实时获取混凝土热学参数变化规律。对于在寻找函数最优值时传统的人工蜂群算法存在求解速度慢、容易陷入局部最优等问题,通过引入全局最优解,并与遗传算法中的交叉操作相结合建立了引入交叉算子的全局人工蜂群优化算法。考虑冷却水管多档通水和外界气温等因素,将改进的全局人工蜂群算法应用于白鹤滩拱坝热学参数反演,分析结果表明基于改进全局人工蜂群算法反演计算的混凝土温度与实测值吻合较好,改进全局人工蜂群算法在拱坝热学参数反演中具有较好的适应性。

Affected by factors such as ambient temperature,cooling water,and surface insulation,the actual thermal parameters of arch dam during construction differ remarkably from laboratory test results.Based on temperature data obtained by using distributed optical fiber sensor,we employed the cross-global artificial bee colony(CGABC)algorithm determine the concrete thermal parameters of Baihetan double-curvature arch dam and capture their real-time variations.To address the slow convergence and susceptibility to local optimals encountered by traditional artificial bee colony(ABC)algorithm in obtaining the optimal function value,we developed the CGABC which integrates the concept of global optimal solutions from particle swarm optimization(PSO)and the cross-operation strategy of genetic algorithm(GA).By considering the influence of multi-stage cooling water and environmental temperature,we employed CGABC for the inversion of concrete thermal parameters of Baihetan arch dam.The inversion results demonstrate a favorable agreement between CGABC-calculated values and measured temperatures.In conclusion,CGABC exhibits excellent adaptability in the thermal parameter inversion of arch dams.