Prediction of the roof weighting using the parameters back-analysis method combined with genetic algorithms
Owing to the many unknown parameters and complex overlying stratums structure evolution, the back analysis of the dynamic deformation is becoming a typical complex nonlinear optimization problem.In order to overcome the low efficiency of the conventional inversion method in multi-parameters back analysis,firstly, using the strata control theory to analyze the dynamic structure evolution of overlying stratums, and choosing the global search optimization algorithm-genetic algorithm,besides combining the implicit nonlinear relationship between supporting resistance and the multi parameters of the overlying rock to find the minimum between field measured value and numerical calculation values, and the unknown lithologic parameters were determined when the minimum was achieved.Among these unknown parameters, including the rock mass density, mechanical parameters—elasticity modulus and poisson ratio, and strength parameters—cohesion and internal friction angle.Lastly, choosing the No.8206 working face in Qingciyao coal mine as the testing site,the hard stratum fracture overlying which always lead to the rapidly increase of supports resistance, and combining the hydraulic supporting resistance value variation with time, and using the algorithm to acquire the optimal parameters and instability rules of the main roof, and the simulation results show a good agreement with the measured results.Then, in this study, using the optimal numerical model can well analyze the fracture characteristic of the overlying stratums and the variation of the hydraulic supports with the different advance distances.
genetic optimization algorithm overlying stratum deformation unknown parameters back analysis surrounding rock mass instability prediction technology
CHEN Yue-du LIANG Wei-guo YANG Jian-feng LIAN Hao-jie Ning-Xiao
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China;Key Laborator College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
国内会议
安徽淮北
英文
61-75
2017-10-14（万方平台首次上网日期，不代表论文的发表时间）