Fuzzy random reliability model establishment of freezing shaft lining structure under big data environment

Aiming at the shortcomings of the conventional reliability in characterization of deep underground structure stability, the fuzzy random reliability model of the whole shaft structure is proposed which is more consistent with the actual working condition. The research results show that: taking the engineering data of reinforced concrete freezing shaft in the Huainan and Huaibei Mining Area as the sample data set and combining the big data HMM model and EM algorithm, researching fuzzy random analysis with shaft external load and shaft ultimate bearing capacity, the analytic model of fuzzy random reliability is obtained for overall shaft lining structure of reinforced concrete in deep alluvium. In addition, the fuzzy random reliability of big data represents the reliability of the whole structure of different depth by interval value. For example, the extreme value of the fuzzy random reliability interval in the 426～483 m section of the lining shaft depth is 0.45% smaller than that of the conventional reliability calculation result and 0.53% larger than that of the conventional reliability calculation result.This method takes into account the stress characteristics of underground engineering structures in a gradual fuzzy process from the effective state to the failure state, thus can reflect the working condition better of deep shaft compared with the conventional single value representation method, and its performance is more reasonable.