Development of a site-specific regression model for assessment of road-header cutting performance of Tabas coal mine based on rock properties

In underground excavation, where the roadheaders are employed, a precise prediction of the roadheader performance has a vital role in the economy of the project. In this paper, a new model is developed for prediction of the roadheader performance using the nonlinear multivariate regression analysis. This model is able to estimate the instantaneous cutting rate (ICR) of roadheader based on rock properties such as Brazilian tensile strength (BTS), rock mass cuttability index (RMCI), and alpha angle (α: is the angle between the tunnel axis and the planes of weakness). In order to construct and test the proposed model, a database including 62 cutting cases is used in the Tabas coal mine No. 1 in Iran. Various statistical performance indices were employed to evaluate the model efficiency. The results obtained indicate that the proposed nonlinear regression model can be efficiently used to predict the roadheader cutting performance. Furthermore, the prediction capacity of this model is better than the empirical models developed previously. Finally, it should be noted that the developed model is site-specific, and it can be used for preliminary estimation of ICR in future phases of Tabas coal mine No. 1. The outcome of this model can be helpful in adjustment of time-scheduling of the project.