In initial phase of mining and civil projects, rock mass rating (RMR) and tunneling quality index (Q) classification systems are widely used. Because of specifying these values, it is possible to predict various rock mass characteristics, such as geomechanical parameters. Hence, many researchers developed experimental models so that the RMR is obtained from Q. The predicted RMR is close to the real value in some domains of Q, but for some domains it is deviated from the real value. Therefore, designers are confused to choose the optimal results of the models. The purpose of this paper is to develop an optimal model for determining RMR various domains of Q. Hence, firstly, the performance of the previous models is evaluated in six different domains of Q and then the best models for each domain are selected. Using the simple regression method, an optimal experimental model is developed based on the selected model in each domain. For this purpose, 214 datasets in the available literatures are used. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) indicators are used to evaluate the performance of the models. Investigations show that the results of the developed model are more reliable than the other models for all of the domains. Therefore, the proposed models can be used as general models in all domains.

在采矿和土木工程的初始阶段，广泛使用岩体质量评级（RMR）和隧道质量指数（Q）分类系统。由于指定了这些值，因此可以预测各种岩石质量特征，例如岩土力学参数。因此，许多研究人员开发了实验模型，以便从Q获得RMR。在Q的某些域中，预测的RMR接近实际值，但对于某些域，它偏离了实际值。因此，设计人员困惑于选择模型的最佳结果。本文的目的是开发一种确定RMR各个Q域的最佳模型。因此，首先，在六个不同的Q域中评估先前模型的性能，然后为每个域选择最佳模型。使用简单的回归方法，基于每个域中选择的模型来开发最佳实验模型。为此，使用了现有文献中的214个数据集。均方根误差（RMSE）和绝对绝对百分比误差（MAPE）指标用于评估模型的性能。调查表明，对于所有领域，开发模型的结果都比其他模型更可靠。因此，提出的模型可以用作所有领域的通用模型。