Fractures are widely distributed in karst areas, and when flow rates are high, they exhibit complex nonlinear behavior that cannot be accurately described by Darcy’s law. In this work, a hydro-mechanical coupling model based on a discrete fracture network is proposed to predict tunnel water inflow, accounting for the impact of non-Darcy flow. The model’s feasibility has been validated by comparing it with experimental results and the field measurements of flow rates at the Bodaoling Tunnel in Guizhou, China. The results show that Darcy flow tends to overestimate water inflow by approximately 25% compared to non-Darcy flow. The non-Darcy effect grows with the increase in initial fracture width and empirical constant q. When q exceeds 8.77 × 10⁻⁶, the growth rate of the Forchheimer number along the fracture width slowed down, and the inhibitory effect of non-Darcy flow on flow became gentle. Additionally, in a complex fracture network, the inflow rate limited by non-Darcy flow at one point drives the water flow through a connect fracture to another point, which increases the difficulty in water inflow prediction. This work highlights the importance of non-Darcy flow and fracture networks when accurately predicting water inflow in tunnels.

Rockburst is a serious disaster caused by the sudden release of rock energy during underground construction in high-stress environments, resulting in severe damage to underground structures. Accurately predicting rockburst intensity is challenging, and establishing a reliable and precise prediction model is of great importance. In this study, we proposed a novel hybrid model for predicting rockburst intensity by integrating rough set theory and multidimensional cloud model uncertainty reasoning. The key steps of the proposed method are as follows: (1) Rockburst cases are collected, and the maximum shear stress \({\sigma }_{\theta }\), uniaxial compressive strength \({\sigma }_{c}\), uniaxial tensile strength \({\sigma }_{t}\), and elastic energy index \({W}_{et}\) are used as predictors for rockburst strength. (2) The Shannon entropy method is used to determine the weights of the four indicators, and a rockburst potential expression is constructed. (3) Rough set theory is used to reduce the number of indicators to construct a rockburst strength prediction rule library. (4) Qualitative data are transformed into quantitative data by using the rules library and multidimensional cloud model to establish an uncertainty inference framework for predicting rockburst strength. Finally, we compared the performance of the hybrid model with existing models, and the results demonstrate that the proposed approach achieves similar or even higher prediction accuracy. The use of cloud droplets in the model offers a significant advantage in the prediction of mixed rockburst intensities, enabling intuitive, rapid, and effective determination of the occurrence intensity of rockbursts.