## 1 Introduction  
The brittleness of rock should be measured as the main property of rock mass in any ground excavation project. It is important to properly consider the brittleness of the rock to design structures of geotechnical engineering, particularly structures constructed on the rock mass. For example, engineers can use the information on rock brittleness to assess the wellbore performance quality and stability of a hydraulic fracturing job [1, 2, 3]. Furthermore, such information can be used to regulate the mechanical properties of shale rocks well. Meanwhile, Young's modulus and strength of these properties can be defined using certain parameters such as the volumetric fraction of strong minerals [4, 5, 6]. One of the reasons for different disasters due to rock mechanics, such as rock bursts, is brittleness [7, 8, 9]. The literature shows that brittleness can be an effective and significant factor that can predict tunnel boring machines (TBMs) and road header performance [10, 11]. Moreover, this property can effectively define the excavation effectiveness of drilling as a parameter highly affecting coal mining processes [3; 12]. Therefore, measuring rock brittleness is necessary for any ground excavation project [7]. Although all of the above facts had been explained, [PERSON] [13] argued that there was no consensus on measurement and definition of standards for this brittleness. On the other hand, [PERSON] [12] argues that rock brittleness is affected by different properties of the rock. Some researchers have described the relationship between brittleness and ductility inversion or the lack of ductility [14]. [PERSON] [15] defined brittleness as the lack of cohesion in rock particles. Brittleness was defined by [PERSON] and [PERSON] [16] as the inclination of a material, such as rock or cast iron, to split. There are normally six characteristics of highly brittle rock: a large compressive-to-tensile strength ratio, a large interior friction angle, the production of small particles, failure under an insignificant force, high firmness, and producing completely developed characteristics after hardness lab experiments [16]. The relationship between the rock's uniaxial compressive and tensile strengths is a significant subject in rock brittleness index (BI) studies [17; 18; 19]. Nevertheless, the relationship between BI and other rock properties such as Poisson's ratio, internal friction angle, hardness, elasticity modulus, etc. is limited in the literature [20; 21]. There has not enough capability to estimate BI in these models due to them using one or two dependent parameters [12; 22]. Rock brittleness can be approximated using empirical formulas proposed by several studies [20; 23; 24]. Alternatively, multi-input and single-input predictive methods such as multiple and simple linear regression can be used to predict the BI value of rock [22; 24]. However, despite a higher accuracy than the existing simple regression [17; 25], they sometimes cannot accurately describe complex systems' behavior since they are not always robust enough [26]. Furthermore, rock BI cannot be predicted due to the insufficient accuracy level of these models [22]. Recently, many researchers have applied machine learning (ML) methods and metaheuristic algorithms to solve engineering and science problems [27; 28; 29; 30; 31; 32; 33]. Despite some researchers confirming that ML techniques could be used to solve problems in engineering fields, studies with a focus on the prediction of rock BI have not used different ML techniques yet. [PERSON] and [PERSON] [34] used Poisson's ratio, velocity, and elastic modulus to apply a neural network (NN) method. [PERSON] and [PERSON] [17] formed a fuzzy system and conducted a multiple regression method to estimate rock BI by using different input parameters such as Brazilian tensile strength (BTS). Their findings demonstrated the effective application of the fuzzy system to estimate BI. [PERSON] et al. [25] suggested some models that predict rock BI value.