Improperly chosen temperature control measures during concrete dam construction can lead to temperature-induced cracks in the concrete.Combining intelligent optimization algorithms with temperature control measures allows for the comprehensive analysis of multiple temperature control factors and intelligent adjustment of these measures.This study proposed a hybrid optimization algorithm IABAP,which integrates both particle swarm optimization(PSO)and ar-tificial bee colony(ABC)techniques.This hybrid approach addressed the issues commonly encountered with single algo-rithms,such as slow convergence in the later stages and susceptibility to local optimization.The IABAP algorithm was used to optimize the parameters for a typical dam section of the Baihetan Arch Dam.By comparing the computed and measured temperature curves,the most effective combination of temperature control measures was determined.The re-sults demonstrate that after applying the IABAP algorithm to adjust the temperature control measures,the predicted temperature profile exhibits a slower temperature rise rate and lower pouring temperature compared to the predictions based on the actual parameter values used during construction.This adjustment is beneficial for managing the early tensile stress in the concrete.The research results provide valuable insights into the use of hybrid optimization algorithms for in-telligent temperature control measure selection in dam construction.
关键词
IABAP混合优化算法/热学参数反演/温度场分析/温控曲线优化/温控措施优选
Key words
IABAP hybrid optimization algorithm/thermal parameter inversion/temperature field analysis/optimi-zation of temperature control curve/optimization of concrete temperature control measures
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