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Roof fall is one of the most important problems connected with underground coal mines because it plays a significant role in financial and human losses. Hence, it is essential to accurately predict the roof fall rate for the purpose of controlling, reducing, and/or even eliminating the risk of the involved problems. On the other hand, there are many different parameters that make a considerable impact on the occurring roof rate. Most of these factors are not completely known or measurable. Therefore, the problem of predicting roof fall is vague, sophisticated, and complex. Adaptive neuro-fuzzy inference system (ANFIS) is a powerful and robust tool for modeling linear and non-linear patterns in science and engineering problems. In this paper, the ANFIS system is applied to model the roof fall rate in coal mines. The constructed model uses the subtractive clustering method to generate fuzzy rules based on 109 data of roof performance from US coal mines. The results demonstrate that prediction of roof fall rate by the ANFIS model is satisfactory. [ABSTRACT FROM AUTHOR]

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength. [ABSTRACT FROM AUTHOR]