水利水电技术2017，Vol.48Issue(6)：48-53,6.DOI:10.13928/j.cnki.wrahe.2017.06.010
基于并行遗传算法和温度荷载的隧洞参数反演研究
Parallel genetic algorithm and temperature load-based study on inversion of tunnel parameter
摘要
Abstract
Aiming at the problem that the temperature load is prone to be neglected in the inversion calculation made on the operation period of tunnel project and taking the deeply embedded tunnel of a large hydropower station in the Southwest China as the study case,the inversions on the parameters of the tunnel surrounding rock and the external water head during the operation of the hydropower station are made with the parallel genetic algorithm by taking the measured values of the reinforcement stress and the increments of the surrounding rock parameters as the basic data for the inversions under the consideration of the coupling effect of both the stress field and seepage field.The comparisons made between the calculation result and the measured one show that the impact from the temperature load on the tunnel parameter value obtained from the inversion is larger;from which the error is lowered from 38.73％ when temperature is not considered to 3.90％ under the consideration of the temperature,while the error of the elastic modulus in the surrounding rock parameters is lowered from 7.35％ to 3.90％ and the error of the internal friction angle is lowered from 7.45％ to 5.76％ as well.The parallel genetic algorithm can be applied to the inversion of tunnel parameter,by which the calculation efficiency is enhanced by 4 times.Application of temperature load can makes the calculation from the inversion more close to the measured value and the inversion result is more correct.The study result can provide a reference for the similar oroject.关键词
温度荷载/并行遗传算法/应力渗流耦合/隧洞/参数反演Key words
temperature load/parallel genetic algorithm/stress-seepage coupling/tunnel/parameter inversion分类
建筑与水利引用本文复制引用
张九丹,任旭华,张继勋..基于并行遗传算法和温度荷载的隧洞参数反演研究[J].水利水电技术,2017,48(6):48-53,6.基金项目
国家科技支撑计划“南水北调中东线工程运行管理关键技术及应用”(2015BAB07B10) （2015BAB07B10）
国家自然科学基金项目(51409170) （51409170）
国家重点研发计划(2016YFC0401801) （2016YFC0401801）
