Uncertainty characterization of surrounding rock parameters is the fundamental cornerstone of tunnel long-life design,and the key is to obtain sufficient accuracy under limited data samples.To address this,a novel method for uncertainty characterization of surrounding rock parameters has been proposed by combining the Bootstrap method and the Akechi Information Criterion (AIC),studying the minimum sample size required to obtain sufficient accuracy.Firstly,the mean and standard deviation of surrounding rock parameters was obtained by the Bootstrap method. Secondly,the probability distributions of the sample under this resampling size were identified by the AIC.Thirdly,the confidence intervals for the mean and standard deviation of the parameters with a confidence level of 95％ were calculated. Subsequently,the minimum numbers of samples required for an accuracy of 90％ were determined.By this way,the curacy of the uncertainty characterization of surrounding rock parameters was ensured. The proposed method was illustrated through Hoek's classical weak rock parameters. Results indicated that the minimum sample sizes for the mean and standard deviation of weak rock parameters are 12 and 22,respectively. These minimum sample sizes derived from the proposed method were validated by real data of weak rocks from two different places,and the results agreed well with the real data.Furthermore,by incorporating the triple standard deviation criterion,this proposed method was applied to conduct uncertainty characterization of surrounding rocks for the third and fourth level rock mass in the rock mass classification standards. The minimum number of samples for weight,deformation modulus,cohesion,internal friction angle,and Poisson's ratio,were obtained. These could provide valuable insights for the uncertainty characterization of surrounding rock parameters in engineering practices,which in turn would aid in tunnel reliability assessments and long-term design considerations.
tunnel engineeringsurrounding rock parametersbootstrap methodminimum sample sizeuncertainty characterizationAIC
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