Determination of Integrity Index <i>K_v</i> in CHN-BQ Method by BP Neural Network Based on Fractal Dimension <i>D</i>

Abstract

The integrity index Kv is the quantitative index in the CHN-BQ method, which can be determined by the acoustic wave test, volume joint number Jv, 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 Kv is proposed to determine Kv 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 Kv is established based on the geological information from 80 sampling windows in Mingtang Tunnel. To further consider the effect of structural plane conditions on Kv, a BP neural network is constructed with the fractal dimension D and structural plane condition index R3 as input and Kv 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 Kvp and measured Kvm is 0.93, and the average relative error is 7.51%. In addition, the predicted Kvp from the 39 groups of data is compared with the Kvd determined directly by fractal dimension D. It can be found that the Kvd has a larger error compared with the Kvp, especially in the case of a Kv less than 0.5. Finally, the BP neural network for predicting Kv is applied to the Jiulaopo Tunnel. The maximum relative error between the measured Kvm and the predicted Kvp is 5.13%, and the average relative error is 2.71%. The BP neural network is well trained and can accurately predict Kv based on the fractal dimension D and the structural plane condition index R3.

Keywords

• integrity index
• fractal dimension
• structural plane network simulation
• back-propagation (BP) neural network