# Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method
## 6 Conclusions  
This paper collects data from three different direct shear tests. When roughness is processed, three experiments use three methods to estimate the roughness. When the joint compressive strength is processed, the test of the two-body interface is included, and the \(JCS_{a}\) and \(JCS_{b}\) of the two-body interface are converted into \(JCS_{ab}\) according to the sharing coefficient. A GA-BP four-factor model was established by using \(\sigma_{n}\), \(JRC\), \(JCS\), \(\varphi\); the GA-BP  
Figure 10: The error diagram of prediction results of each model: (**a**) JRC-JCS model error; (**b**) GA-BP four-factor model error; (**c**) GA-BP five-factor-1 model error; (**d**) GA-BP five-factor-2 model error; (**e**) GA-BP six-factor model error.  
five-factor-1 model was established by using \(\sigma_{n}\), \(JRC\), \(JCS\), \(\varphi\), _size_; the GA-BP five-factor-2 model was established by using \(\sigma_{n}\), \(JRC\), \(JCS\), \(\varphi\), \(v_{n}\); and a GA-BP six-factor model was established by using \(\sigma_{n}\), \(JRC\), \(JCS\), \(\varphi\), \(size\), \(v_{n}\). According to the number of different factors or the type of factors, the model with the best prediction effect was determined. The main conclusions are the following:  
(1) The peak shear strength of the rock joint is highly correlated with normal stress and roughness of the rock joint. The peak shear strength of rock joints can be improved by enhancing normal stress or increasing roughness.  
(2) A BP neural network optimized by genetic algorithm can effectively predict the shear mechanical properties of jointed rock. The average error of multi-factor prediction was controlled at 4.5%. The quantitative mapping relationship between rock factors and peak shear strength was constructed, and an intelligent prediction method of peak shear strength of rock was formed.  
(3) The higher the correlation between the input factors and the output results of the GA-BP neural network, the greater the relevance of the input factors, and the better the prediction accuracy of the model.  
In this paper, a GA-BP neural network is applied to the prediction of joint shear strength, and this idea is novel. Compared with the JRC-JCS model, the GA-BP neural network model has better prediction accuracy in all aspects. Among the four GA-BP neural network models, the six-factor model is better. Due to the limitations of the data, no factors such as shear rate were added to the input layer, and no more rock types were input. Adding more influencing factors to the GA-BP neural network model and increasing the range of data can improve the accuracy of prediction to a greater extent, which will be the goal of future work.  
Methodology, H.L.; validation, A.S.; data curation, Z.Z.; writing--original draft preparation, C.Z.; writing--review and editing, B.G. Formal analysis, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study did not require ethical approval.  
Not applicable.  
No new data were created or analyzed in this study.