Jingqi Lai

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.