# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 1 Introduction  
In underground space and excavation related projects, brittleness of the rock is considered as one of the most important properties of the rock mass. Having an appropriate insight on rock brittleness in other fields of engineering also help engineers alleviate the issues related to brittleness. For example, the acquisition of sufficient knowledge on the rock brittleness by oil and gas engineers could helpthem to evaluate the wellbore stability as well as appraise the performance of a hydraulic fracturing job [1]. Moreover, the brittleness regulates the properties of the shale rocks mechanic. At the same time, by employing several parameters such as the carbonates, volumetric fraction of strong minerals, weak elements and pores, Young modulus and strength of these properties can be defined [2]. In deep underground engineering, brittleness is a critical factor to assess the stability of the surrounding rock mass [3]. Besides, many disasters related to rock mechanics like rock-bursts may stem from brittleness [4; 5; 6]. Several studies showed that brittleness is also an important factor to estimate the tunnel boring machine (_TBM_) and road-harder cutting performance [7]. In addition, it defines the excavation efficiency of drilling, which considerably influences coal mining [8]. Hence, the assessment of rock brittleness is necessary for geotechnical and rock mechanics projects [5]. However, despite the fact that the brittleness is an important parameter for designing civil and mining engineering projects, but according to [PERSON] [9], there is still no consensus on definition and measurement standards of this phenomenon. Hence, according to [PERSON] [10], various rock properties influence rock brittleness. Some studies have related brittleness to the lack of ductility or ductility inversion [11]. [PERSON] [12] also defined the brittleness as breaking of inter-particle cohesion of a rock. In addition, [PERSON] and [PERSON] [13] pointed out that brittleness is the inclination of a material, such as cast iron or many types of rocks, to be split following pressure equivalent or higher than the material yield stress. A highly brittle rock typically has the following features: (a) failure without a considerable force, (b) generation of small particles, (c) great ratio of compressive to tensile strength, (d) great firmness, (e) great interior friction angle, and (f) production of fully developed fractures following hardness lab experiments [14]. It seems that the majority of studies that were conducted on rock brittleness index (_BI_) were based on relationship between tensile and uniaxial compressive strengths of the rock samples [15; 16; 17]. However, a few studies have presented and suggested a relationship between _BI_ with other rock properties such elasticity modulus, hardness, Poisson's ratio, internal friction angle, and quartz content [18]. The performance of these models have reported not capable enough to predict the _BI_. This is because of the fact that most of them used one or two dependent parameters [8; 17]. It seems that the use of multi-inputs predictive systems in estimating _BI_ of the rock would be great to receive a higher degree of accuracy compared to simple regression models. Recently a large number of studies used soft computing (SC), machine learning (ML) and artificial intelligence (AI) techniques to solve problems related to science and engineering fields [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. However, a limited number of studies relevant to these methods have been conducted to predict the rock _BI_ in literature. [PERSON] and [PERSON] [40] employed an artificial neural network (ANN) technique to predict the rock _BI_ using system inputs such as the velocity of the S and P waves, Poisson's ratio, elastic modulus and unit weight. [PERSON] and [PERSON] [8] estimated the rock _BI_ by constructing fuzzy inference system (_FIS_) and non-linear regression analysis.