Yuechao Pei

In rock tunnel engineering, the discontinuity roughness plays a crucial role in rock mass stability. Nevertheless, no suitable parameter is currently available to describe the multi-scale morphological complexity associated with varying shear directions. Improved 1D and 3D fractal dimensions are proposed and systematically evaluated for their ability to characterize the anisotropy and morphology complexity. The results show that the trends of improved 1D and 3D fractal dimensions are consistent with the Grasselli parameter GP(θ) along the orthogonal direction of the major and minor axes, which effectively characterizes the anisotropy. Meanwhile, they effectively characterize the profile and discontinuity morphology complexity, respectively. On the basis of the evaluation results, a comprehensive parameter is proposed to quantify directional multi-scale morphological complexity, which characterizes the discontinuity anisotropy, local roughness, and global morphological complexity simultaneously. Furthermore, the proposed parameter is compared with GP(θ) based on the Huashansong tunnel, using the concordance correlation coefficient and macro-trend similarity as evaluation criteria. The results show a moderate correlation between the two parameters, with a concordance correlation coefficient of 0.716. The overall similarity score of 85.8 and the macroscopic trend similarity of 71.0 indicate strong consistency in morphological features. In addition, the RMSE and MAE of γ(θ) are 0.25 and 0.19, which is lower than that of the existing GP(θ) and PSD-based methods in the study of characterizing the dominant morphology complexity direction.

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.

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.