Determination of Integrity Index Kv in CHN-BQ Method by BP Neural Network Based on Fractal Dimension D

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 R-3 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 R-2 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 R-3.