# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 1 Introduction  
Cracks in rock can be divided into tensile and shear cracks. A tensile crack is caused by the separation of two relative crack surfaces along the normal direction, while a shear crack is caused by the slip between two expressed crack surfaces along the shear direction. A good understanding of tensile and shear crack development can provide information to reveal rock failure mechanisms [1], which is beneficial to the early warning of rock failure or instability in rock engineering. Consequently, it is crucial to investigate tensile and shear crack development during the rock failure process. It is difficult to observe crack development during rock failure directly with the naked eye or optical imaging instruments. Fortunately, acoustic emission (AE), an acoustic signal, can be used to investigate crack development indirectly. Two methods based on AE have been proposed to recognize the tensile and shear crack types during the failure of brittle material. The first is the AE parameter method [2]. In this method, the ratio of the AF (average frequency) and the RA (rising time/amplitude) of the AE are taken as the indicator to discriminate the crack types. This method has already been used to investigate the characteristics of the tensile and shear cracks during the failure of concrete and coal [3, 4]. The second is the moment tensor method [5]. Compared with the AE parameter method, the moment tensor method is more complicated, but it can obtain a more comprehensive result. By using the moment tensor method, not only the crack type but also the location and orientation of the crack can be obtained. Although these two methods can recognize the crack type, there are still some limitations to these techniques. The first is that the sensor for monitoring the AE signal must be attached near the failure region, due to the rapid attenuation of the AE signal during its propagation, especially when a structuralplane is encountered. In this situation, the sensor is easily damaged. When the sensor is too far from the failure region, the obtained AE signals will be of low quality. The second is that the AE signal cannot be completely collected during rock failure. This is because the macro cracks that form before rock failure will hinder the propagation of an AE signal [6; 7; 8; 9]. This is why a quiet period of AE signals is observed during some rock failures. Hence, a more useful method should be developed to recognize the types of cracks generated during rock failure. Sound is also an acoustic signal and can be easily obtained during rock failure. By using sound, the cracking process during rock failure can also be investigated. There are some differences between AE and sound signals. First, the frequency of sound is lower than that of AEs. In rocks, the frequency of sound ranges from 20 to 20,000 Hz, and that of AEs ranges from 20,000 to 100,000 Hz, as shown in Figure 1. Sound can be detected by the human ear, but AE signals must be monitored by a specific sensor. Second, sound can propagate in the air and can be monitored with a non-contact method. Even if the monitoring device is far from the source, the sound signal can still be recorded accurately, which can provide a safe environment for the sensors and workers. In addition, the sound signals not only overcome the effects of heterogeneous geological formations (resulting in rapid signal attenuation or large spatial variability) but also avoid practical problems such as the decrease in accuracy and reliability of signal acquisition caused by the inadequate contact between the acoustic emission sensor and the rock mass. Hence, using sound to investigate rock failure is less expensive than using AE. However, sounds have seldom been used in rock failure investigations. Only the qualitative description of sound intensity has been employed to characterize the grade of rockburst [10; 11; 12]. The identification of the crack types by using sound during the rock failure process has not yet been studied.