During the classification of surrounding rock in tunnel engineering

different results are normally obtained by the same evaluation system and classification standard. It is caused by the uncertainty of rock parameters and the discreteness and randomicity of evaluation indices

which are resulted from the instrument error and artificial operation in the testing process. Especially in the sub-classification of surrounding rock

evaluation results usually have poor robustness

even grade skipping. Hence

it is necessary to investigate the reliability of surrounding rock classification. Based on the handbook of Engineering Rock Classification Standards

we analysed the probability distribution function of rock strength and rock mass integrity index. The system reliability analysis theory was also introduced to build the function of different surrounding rock grades. Moreover

Monte Carlo method was applied to calculate the reliable probability belonging to different evaluation levels. Finally

the reliability analysis method was proposed for the sub-classification of surrounding rock

according to the basic quality (BQ) method. Since this method takes into account the uncertainty and discreteness of the evaluation indices sufficiently

the calculated reliability indices can make a more steady evaluation result. Besides

the reliable probability can reflect the dispersion degree of working face information of the developed fault fracture zone and the weak interlayer to a certain extent. This method was applied to the surrounding rock sub-classification of Laohushan tunnel in Jinan belt highway. It was found that the evaluation results were consistent with the actual levels of surrounding rock. Besides

the continuity of reliability indices at different surrounding rock levels can quantitatively express the changing conditions of geological attributes during the tunnel construction. Therefore

this study provides data support for the transformation of construction method and the optimisation of supporting parameters. Research results have significant references for surrounding rock sub-classification in large-cross-section tunnels.