CAJ | 학술논문

对于大体积混凝土最优温控方案的选取，传统方法是按照规范的要求和人工反复修改方案，存在效率低下和受限于经验的问题。该文采用改进的微粒群算法（PSO，particle swarm optimization）优化方法，以及基于有限元（FEM，finite element method）的混凝土温度场和应力场仿真算法联合进行优化。算例设定了2个优化目标，即只考虑安全性的单目标优化（多特征点达到最小防裂安全系数1.8），以及考虑安全性和经济性的双目标优化（温控综合成本最小化）。计算结果表明，所提方法能够实现温控方案的自动寻优，优化结果更科学合理，总体研究效率可提高50%以上。考虑双目标优化后，在确保防裂安全的条件下能够明显降低温控措施的综合成本。
대우대체적혼응토최우온공방안적선취，전통방법시안조규범적요구화인공반복수개방안，존재효솔저하화수한우경험적문제。해문채용개진적미립군산법（PSO，particle swarm optimization）우화방법，이급기우유한원（FEM，finite element method）적혼응토온도장화응력장방진산법연합진행우화。산례설정료2개우화목표，즉지고필안전성적단목표우화（다특정점체도최소방렬안전계수1.8），이급고필안전성화경제성적쌍목표우화（온공종합성본최소화）。계산결과표명，소제방법능구실현온공방안적자동심우，우화결과경과학합리，총체연구효솔가제고50%이상。고필쌍목표우화후，재학보방렬안전적조건하능구명현강저온공조시적종합성본。
For the selection of temperature control measures for massive concrete, traditional methods are fully in accordance with the industry standard requirements and subject to repeated artificial amending by practical experience in engineering design and construction. Therefore, it is inefficient and limited by the designer's experience. In this paper, an improved particle swarm optimization (PSO) combined with concrete temperature field and stress field based on the finite element method (FEM) was tested to select the optimal concrete temperature control measures. In the simulation cases, two optimization objectives were defined. The first objective was that the tensile stress of multi feature points should satisfy a safety-cracking factor of at least 1.80. The second was that the whole temperature measures cost should be minimal. The optimization computation was implemented separately with only a single objective for safety factor and both objectives for safety and cost. From the results of 6 calculation cases for a fictitious small-scale concrete dam structure, the following conclusions were drawn. 1) The results show that the proposed method can achieve automatic finding of the temperature control measures optimization, and the optimization results are more scientific and more reasonable. If the ranges of the temperature control parameters can be defined reasonably, the dependence of optimization on humans can be decreased. It will increase the scientificity and persuasion of the temperature control scheme, especially under the complicated situation of more optimization objects, which is very difficult to draw a most reasonable quantitative measures composition. 2) The efficiency of the whole research is improved noticeably. According to experience, if the safety factor is taken as the only objective, the optimization will cost 5 to 7 days by a medium level researcher. In this paper, the time cost of the intelligent optimization is only 2.2 days. 3) After considering the two-objective optimization, the total costs of temperature control measures can be significantly reduced by at least 9%under the condition of ensuring crack-prevention safety. 4) The total calculation time will be influenced by the types, the number and changes of temperature control measures, the locations and number of feature points, and the number of optimization objectives. A high performance personal computer is tested in this paper. The optimization time cost of 5 feature points and 300 days of simulation is 2.7 times the one of 3 feature points and 80 days of simulation. The optimization time cost of the dual-objective is 1.6 times the single-objective. Therefore, a high performance parallel machine should be used to implement the proposed intelligent method for a large-scale engineering structure in a multi-objective, multi-measure, and multi-feature-point optimization task. 5) If the equivalent cooling pipe algorithm is adopted to replace the current explicit one, the optimization for pipe distances will become more feasible. 6) The cost of different temperature control measures in this paper may not be suitable for every construction site. In a factual application case, the checked prices and cost weight should be considered. For future research, how to define the reasonable weights for different feature points at different locations of different structures is the next challenge.