CAJ | 학술논문

隧洞开挖过程中围岩监测断面的布置一般滞后于掌子面开挖,监测断面布置前围岩已发生的位移称为损失位移.采用优化反分析思路求取损失位移,该思路将损失位移的求解转化为以实测位移与计算位移的误差作为目标函数、岩体力学参数作为决策变量的全局优化反分析问题.针对该全局优化反分析问题是一类高度非线性多峰值且计算代价较高的优化问题,将性能优异的粒子群优化算法与高斯过程机器学习方法相融合,结合FLAC3D数值计算程序,提出隧洞围岩损失位移优化反分析的粒子群-高斯过程-FLAC3D智能协同优化方法.算例研究表明,该方法是可行的,不仅能获得可靠的损失位移预测结果,而且可获取合理的围岩计算模型力学参数,具有全局性好、计算效率高的特点,克服了传统优化反分析方法容易陷入局部最优或过于依赖初始学习样本的局限性.将该方法应用到锦屏二级水电站辅助洞BK14+599断面的损失位移反分析,获得了该断面围岩的损失位移和力学参数,其中,损失位移较大,原因在于岩体开挖后在短时间内弹性变形大.因此,对于地下工程,特别是深部地下岩体工程,在围岩稳定性评价与围岩参数反分析中,损失位移不可忽视,应给予足够重视.
수동개알과정중위암감측단면적포치일반체후우장자면개알,감측단면포치전위암이발생적위이칭위손실위이.채용우화반분석사로구취손실위이,해사로장손실위이적구해전화위이실측위이여계산위이적오차작위목표함수、암체역학삼수작위결책변량적전국우화반분석문제.침대해전국우화반분석문제시일류고도비선성다봉치차계산대개교고적우화문제,장성능우이적입자군우화산법여고사과정궤기학습방법상융합,결합FLAC3D수치계산정서,제출수동위암손실위이우화반분석적입자군-고사과정-FLAC3D지능협동우화방법.산례연구표명,해방법시가행적,불부능획득가고적손실위이예측결과,이차가획취합리적위암계산모형역학삼수,구유전국성호、계산효솔고적특점,극복료전통우화반분석방법용역함입국부최우혹과우의뢰초시학습양본적국한성.장해방법응용도금병이급수전참보조동BK14+599단면적손실위이반분석,획득료해단면위암적손실위이화역학삼수,기중,손실위이교대,원인재우암체개알후재단시간내탄성변형대.인차,대우지하공정,특별시심부지하암체공정,재위암은정성평개여위암삼수반분석중,손실위이불가홀시,응급여족구중시.
@@@@The monitored sections are always assembled behind working face excavation. The displacement induced during this period is called loss displacement. The optimization back analysis method is used to get loss displacement. The method transforms the problem to a global optimized problem that treats the error between geodesic loss displacement and computational loss displacement as objective function, the mechanical parameters of surrounding rocks as decision variables. Aiming to solve the global optimized problem that is high nonlinearity, many peak values and expensive cost, an intelligent cooperative optimization algorithm based on particle swarm optimization (PSO) and Gaussian process (GP) machine learning for back analysis is proposed, then combined the FLAC3D, a new method called PSO-GP-FLAC3D for the loss displacement back analysis is developed. The results of a numerical example show that the proposed method is feasible. It not only obtains reliably predicted loss displacement, but also gets reasonable mechanical parameters of surrounding rocks. In addition, the proposed method has the merits of global optimization and high computational efficiency. It can overcome the shortcomings that the traditional optimization back analysis method is easy to fall into local optimum or overly dependent on initial learning samples. The proposed method is applied to the auxiliary tunnel BK14+599 section of Jinping Ⅱ hydropower station in China, and loss displacement and mechanical parameters of surrounding rocks are obtained. The results indicate that the elastic deformation of surrounding rocks increased quickly after excavation, which results in large loss displacement. Therefore, the loss displacement of surrounding rocks can not be ignored in stability evaluation or back analysis for underground engineering, especially for deep underground rock engineering.