An analysis framework integrating stochastic stratigraphic modelling and probabilistic analysis in tunnel engineering

In tunnel engineering, the excavation response is significantly affected by stratigraphic uncertainty. Therefore, quantitative evaluation of stratigraphic uncertainty is crucial. In this paper, an automatic framework integrating stochastic stratigraphic modelling and numerical calculation is proposed to investigate the impact of stratigraphic uncertainty on ground surface settlement for tunnel projects. A three-dimensional stochastic stratigraphic modelling approach based on Markov random field and Bayesian machine learning is developed to construct a database of potential strata. Then, by integrating the non-intrusive analysis method, stratigraphic models are automatically mapped onto the numerical model, followed by numerical simulations in batches. A synthetic case study is used to determine the selection of model parameters and validate the reliability of the stochastic stratigraphic modelling approach. Finally, a subway tunnel is selected as a real-world case to demonstrate the effectiveness of the proposed framework. The local stratigraphic uncertainty of various stratigraphic layers is quantitatively measured based on the proposed local geological uncertainty index. A preliminary investigation is conducted on the impact of stratigraphic uncertainty on the distribution of tunnel surface settlement. The results show that a straightforward assessment of tunnel construction can be achieved with a small number of calculations, thus providing effective guidance for subsequent construction optimisation.