Abstract: Accurate inference of the probability distributions of geotechnical parameters is a crucial step for reliability analysis and risk assessment in geotechnical engineering. At present, the probability distributions of geotechnical parameters are mainly inferred based on in-situ and/or laboratory test data. This paper aims to propose an adaptive Bayesian updating approach for the probability distribution inference of geotechnical parameters, in which a quantitative termination strategy for subset simulation is presented. Moreover, a framework for the inference and reliability analysis of the probability distributions of geotechnical parameters is constructed. The effectiveness of the proposed approach is verified by taking the landslide on No. 3 Freeway in Taiwan and a saturated clay slope as examples. Finally, the influence of the number of samples in each subset simulation level on the inference of probability distributions is addressed in this paper. The results indicate that, in comparison with the maximum likelihood and Markov chain Monte Carlo methods, the proposed approach is more efficient in calculation, simpler in programming, and can provide an effective way to solve the problem of probability distribution inference of geotechnical parameters at low acceptance probability levels. The number of random samples in each subset simulation level has certain influence on probability distribution inference. As the number of samples in each level increases, the posterior statistics of geotechnical parameters and threshold of subset simulation gradually converge. In addition, the rationality of the established quantitative termination strategy for subset simulation can be verified according to the variation of complementary cumulative distribution function with the subset simulation threshold.

Key words: geotechnical parameters, probability distribution inference, Bayesian updating, subset simulation, observations