# Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method
## 1 Introduction  
There are many joints in natural rock, and the failure of rock formations has a strong relationship with the strength of rock joints. The shear strength of rock joints is an important indicator in practical rock engineering such as mining, sloping, tunneling and so on. In practical engineering, when the shear strength of the rock joint needs to be established, it must be sampled on-site and is obtained only after a lot of shear tests in the lab or on-site. This process takes a lot of time and manpower to complete. Therefore, if a rock joint shear strength prediction model is established by using existing test data, the estimated rock joint shear strength can be obtained by inputting some related factors, which can save a lot of resources. The factors related to rock joint strength are mainly divided into two types. On the one hand, there are the physical factors of the rock specimen itself, such as uniaxial compressive strength, size, rock joint roughness, basic friction angle, etc. On the other hand, there are the factors related to the shearing instrument during the shearing process, such as normal stress, normal loading rate, etc. Some scholars have conducted extensive research on the relationship between the above factors and rock joint shear strength. Through a large number of laboratory experiments and field observations, [PERSON] first proposed a quantitative description of the joint roughness factor which was the joint roughness coefficient, or JRC, and a peak shear strength model about incorporating \(\sigma_{n}\), \(\varphi\), JRC and JCS was established [1]. [PERSON] et al. found that the mechanical factors of a jointed rock mass have a size effect after the test [2]. [PERSON] et al. carried out direct shear tests on artificial rock joints with different morphologies under constant normal load (CNL) conditions, studied the contribution of different shear components to the total shear resistance, and proposed a new model to evaluate the peak shear strength of rock joints [3]. [PERSON] et al. analyzed the influence of normal loading rate and other factors on the shear characteristics of rock joints. The results show that a higher normal loading rate will make the joints more prone to damage and deterioration, and the peak shear strength of the joints will deteriorate faster [4]. [PERSON] et al. proposed a new JRC evaluation and comparison method to overcome the shortcomings of traditional visual comparison methods based on vector similarity measures (VSMs) [5]. In order to overcome the shortcomings of the existing two-dimensional roughness factors, [PERSON] et al. proposed two factors that represent the local characteristics of the joint profile and the overall characteristics to characterize the roughness of the rock joints [6]. [PERSON] et al. have done experimental research on the shear properties of rocks under different normal stresses. The results show that the linear relationship between normal stress and shear strength is universal [7]. [PERSON] et al. proposed two empirical formulas to predict the peak shear strength of a rock-concrete interface by conducting direct shear tests on rock-concrete interfaces under different normal stresses [8]. [PERSON] et al. summarized JRC and provided a clear perspective for the concepts, methods, applications and trends related to its expansion. They introduced the origin and connotations of JRC, JRC-related roughness measurement, JRC estimation method, JRC-based roughness characteristics survey, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications [9]. The above experiments show that roughness, normal stress, different roughness of the two-body interface and normal loading rate will affect the peak shear strength. In order to predict the peak shear strength, some scholars have established a peak strength prediction formula. [PERSON] et al. carried out more than 50 constant normal load direct shear tests on seven rock types.