Stability classification probability model of loess deposits based on MCS-Cloud

The stability classification of loess deposits around tunnels is a vital prerequisite for safe construction in underground environment. Due to the fuzziness and randomness of loess physical and mechanical parameters, the stability prediction of loess deposits shows uncertainty. Existing loess deposit stability classification models rarely consider the uncertainty of influencing factors. A novel classification probability model of loess deposits is proposed for the above problems based on Monte Carlo simulation and multi-dimensional normal cloud (MCS-Cloud). Specifically, five loess parameters, including water content, cohesion, internal friction angle, elastic modulus, and Poisson ratio, were selected as predictors for the stability level of loess deposits. The weights of the predictors were obtained through 50 test samples. After acquiring the numerical characteristics of the normal cloud, the stability level can be comprehensively evaluated with the weighted multi-dimensional normal cloud model. The classification model was applied to the loess tunnel in Yan'an, China. The prediction results are in good agreement with practical engineering, denoting the rationality of the weighted multi-dimensional normal cloud. Finally, the stability classification of loess deposits was discussed from the perspective of uncertainty analysis with the application of MCS. Results proved that the MCS-Cloud model is feasible for classifying the stability of loess deposits surrounding tunnels. The obtained classification probability can be used for quantitative risk assessment of loess tunnels.