The integrity index K_v is the quantitative index in the CHN-BQ method, which can be determined by the acoustic wave test, volume joint number J_v, or empirical judgment. However, these methods are not convenient and require the practitioner to have extensive experience. In this study, a new quantitative evaluation of K_v is proposed to determine K_v accurately and conveniently. A method for determining the fractal dimension D based on the structural plane network simulation is proposed. A quantitative relationship between fractal dimension D and integrity index K_v is established based on the geological information from 80 sampling windows in Mingtang Tunnel. To further consider the effect of structural plane conditions on K_v, a BP neural network is constructed with the fractal dimension D and structural plane condition index R₃ as input and K_v as output. The BP neural network is trained by 260 groups of tunnel data and validated by 39 groups of test data. The results show that the correlation coefficient R² between the predicted K_vp and measured K_vm is 0.93, and the average relative error is 7.51%. In addition, the predicted K_vp from the 39 groups of data is compared with the K_vd determined directly by fractal dimension D. It can be found that the K_vd has a larger error compared with the K_vp, especially in the case of a K_v less than 0.5. Finally, the BP neural network for predicting K_v is applied to the Jiulaopo Tunnel. The maximum relative error between the measured K_vm and the predicted K_vp is 5.13%, and the average relative error is 2.71%. The BP neural network is well trained and can accurately predict K_v based on the fractal dimension D and the structural plane condition index R₃.

In tunnel construction, predicting the roughness of discontinuity is significant for preventing the collapse of the excavation face. However, currently, we are unable to use a parameter with invariant properties to quantify and predict the roughness of discontinuity. Fractal dimension D is one such parameter that be used to characterize the roughness of discontinuity. The study proposes a new method to predict the roughness of discontinuity from the fractal dimension D of outcrops. The measurement method of the coordinates of outcrops is firstly summarized, and the most suitable method of calculating fractal dimension D is then provided. For characterizing the spatial variability of fractal dimension D, the random field of fractal dimension D is discretized, and the prediction model is then established based on Bayesian theory. The proposed method is applied to one tunnel for predicting the roughness of discontinuity, and the results indicate that the relative errors of prediction are less than 1.5%. The sensitivities of correlation function and discontinuity size are analyzed. It is found that the different correlation functions have no obvious effect on the prediction results, and the proposed method is well applied to relatively large sizes of discontinuity.

This study takes a new Shidao tunnel where the left line constructing in weak rock mass as a case study, and the effect of the new constructing tunnel on the existed tunnel are studied by the numerical analysis. High-precision field investigations are conducted to provide accurate parameters for the numerical model. The modified generalized Zhang-Zhu (GZZ) constitutive model is applied, and the numerical analysis results containing horizontal convergence displacement of side walls of the new constructing tunnel and a longitudinal crack in existed tunnel are validated by real-time monitoring. The vertical displacement of the vault, the horizontal displacement of the adjacent side wall, the plastic zone of the new constructing tunnel are studied. The effect of the new constructing tunnel on the existed tunnel is studied by analyzing the vertical displacement of the vault and the horizontal displacement of the adjacent side wall of the existed tunnel. During the constructing process of new tunnel, the maximum width variation of longitudinal crack is less than 0.3 mm. The maximum vertical displacement of the vault is less than 1 mm, and the maximum horizontal displacement of the adjacent side wall is less than 0.5 mm for the existed tunnel. Finally, the effects of tunnel spacing between new constructing and existed tunnels and geological condition represented by geological strength index (GSI) are investigated. The result shows that the maximum vertical displacements of the vault and the maximum horizontal displacement of adjacent side wall can reach −10.4 mm and −4.9 mm respectively when tunnel spacing is 0.5 d (d is actual spacing). When GSI is increased from 15 to 30, the maximum vertical displacement of the vault is reduced obviously.

In recent years, offshore wind turbine technology has been widely developed, making a significant contribution to the advancement of renewable energy. Due to the predominant subsurface geological composition characterized by rocky formations in some marine areas, rock-socketed piles are commonly applied as offshore wind turbine foundations. Generally, rock-socketed piles need to be driven into rock layers that have not undergone significant weathering or erosion for optimal load-bearing capacity. This design is essential to ensure structural support for offshore wind turbines. However, during the long-term operation period of offshore wind turbines, the contact surface between the rock-socketed pile and the rock is prone to be detached under multiple dynamic loads. The generated channel makes seawater seep into the unweathered rock layer, resulting in the erosion of rock meso-structure and deterioration of mechanical properties. The reduced load-bearing capacity will adversely affect the operation of the offshore wind turbine. In this study, the meso-structural evolution of bedrock in pressurized seawater is investigated by X-ray CT imaging using tuff samples from the marine areas of an offshore wind farm in China. A cellular automata model is proposed to predict the long-term evolutionary process of tuff meso-structure. Results indicate that the porosity of the tuff sample in the pressurized seawater shows an upward trend over time. Based on the erosion rate of pores obtained from the CT scanning test, the proposed cellular automata model can predict the evolutionary process of tuff meso-structure and corresponding failure strength of the bedrock in the long term.