## 1 Introduction  
Jet grouting (JG) has been a widely applied approach for the stabilization of loose materials such as soil, fragmented rock, coal, etc. [1]. Generally, a stabilizing fluid is injected into soft materials under high velocity and pressure [2]. The injecting equipment is composed of a JG string with a nozzle at the end, which can inject the fluid into the soft materials via a rotary motion as the string is raised and rotated slowly [3; 4]. Usually, cementitious grout or chemical grout can be utilized as blinders to consolidate the loose materials in order to improve their mechanical properties, such as unconfined compressive strength (UCS).  
In underground coal mines, the coal-grout composites created by the JG technique were used as a new support material. Due to its cutting and erosion effect, the mechanical properties of the raw coal were completely changed by the injected grout [5; 6]. After the JG process, a rigid coal-grout pile surrounded by coal mass was formed with low deformability and high strength, which can provide a sufficient support for roadways in coal mines [7; 8; 9; 10]. In practice, conducting UCS tests is an extensively used approach to assess the supporting ability of support materials [11; 12; 13]. However, UCS tests are time-consuming and costly since many influencing variables should be considered. To address this problem, an indirect method for evaluating the UCS of supporting materials is to establish empirical equations based on statistical regression [14; 15]. Nevertheless, some empirical rules used for designing JG parameters are often conservative and have limited applicability [12]. In addition, the nonlinear relationship between the UCS and the multiple influencing variables is unknown, which makes it more difficult to develop empirical equations [16; 17]. Therefore, to predict the UCS of coal-grout composites efficiently and accurately, some computational models are necessary.  
In recent years, machine learning (ML) based models have been used extensively in rock materials [18; 19], composite materials [5; 20; 21; 22; 23; 24], and other areas [25; 26; 27]. Due to the high-efficiency performance for data processing, the outputs could be predicted accurately by using the inputs without knowing their relationship, which can reduce the need for time- and money-consuming experiments. However, to the author's knowledge, no relevant studies on the strength prediction of jet grouted coal-grout composites using ML methods have been reported, since the coal-grout composite is a relatively new support material. In addition, compared with the one or two conventional ML methods used in the composites mentioned above, more advanced ML algorithms, such as decision tree (DT) and random forest (RF), should also be applied to study their feasibility in coal-grout composites.  
In the current paper, six algorithms, such as DT, RF, the back-propagation neural network (BPNN), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR), were used to evaluate the UCS of coal-grout composites combining influencing variables (i.e., coal-grout ratio, grout types, and curing time). Some experimental UCS values were used to construct a dataset to train and validate our proposed models. The performances of these algorithms were compared and discussed  
The evaluation of coal-grout composite is of significance before JG application in the field. Therefore, to determine the composite accurately and rapidly, the ML methods were proposed and utilized. Considering several key influence variables and the UCS of coal grout composite, non-linear relations were established by intelligent models. This novel work provides some new and fast approaches to estimate coal-grout composite strength, which can promote the JG design and contribute to the artificial intelligence application.