Abstract Today, the use of stochastic modeling, reliability and simulation techniques is very common in various sciences, including mining engineering. When dealing with parameters that do not have a definite value due to uncertainty and can vary over an interval, the use of stochastic modeling is useful to investigate their behavior. In mining engineering, we usually encounter problems in various fields such as mineral exploitation,supporting systems, ventilation, blasting, etc., which due to the uncertainty in the parameters of which definitive analysis is not possible, so we can say probabilistic analysis is a better way to investigate the impact of uncertainties on engineering design.The simulation technique has also developed a powerful statistical solution that can identify existing problems before they become problematic. Simulation is used when the use of analytical methods is impractical due to the complexity of the problem. As mentioned, reliability modeling in the field of supporting systems, especially in coal mines, is very important because of the importance of these supporting systems in preventing roof fall and reducing casualties and accidents due to roof fall. One of the most widely used of these systems is the rockbolt system. Due to the fact that rockbolt is a widely used tool in coal mines and the use of rockbolt in mines has been increasing over the years, so its performance during mining operations has a significant role for us. Therefore, the most important objectives of this research are to develop a model to estimate the reliability of the rockbolt system in coal mines, to use stochastic modeling to eva‎luate and investigate the role of controllable and uncontrollable parameters on the performance of rockbolt and to identify the most effective parameters on performance. In the first part of this study, using a database of 109 data and Monte Carlo simulations on the reliability equation of the rockbolt system, it was concluded that the number of cases where the rockbolt system failed was very close to reality and about 65%. The reliability function is also proportional to the lognormal distribution function with an average of 1.78 and a standard deviation of 4.08. Also in the second part of this dissertation, the sensitivity analysis of the reliability of the rockbolt system to the input parameters was performed. The result was that the most effective parameter among the 6 input parameters is the coal mine roof rating(CMRR) with an impact of 65.8%. Other influential parameters are the depth of cover ,the bolt length ,the bolt capacity ,the entry width and finally the row spacing of rockbolts.

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