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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]

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]

Abstract: Road header performance prediction plays a significant role in the successful implementation of a tunneling project; so that, there is a need for accurate prediction of the advance rate of tunneling. However, there is relatively less study on predicting the performance of such machinery by using soft computing techniques although they have some advantages over the other methods. On the other hand, often models applied for road header performance prediction neglect interaction between machine and rock mass parameters. The Takagi–Sugeno (TS) fuzzy system model, one of the most popular fuzzy models, can be applied to solve complex problems by transferring a nonlinear system into a set of linear subsystems. However, in many situations, it is not convenient to identify all the rules; so, using the fuzzy clustering techniques in which the rules are resulted from measured data can be useful and valuable. In this paper, a new model based on the geological and geotechnical site conditions is developed to predict the road header performance. The model is developed using soft computing technique that applies the concept of fuzzy logic to take into account the uncertainty and complexity derived from the interaction between rock properties and road header parameters. The prediction capabilities offered by TS fuzzy model based on subtractive clustering method are demonstrated by using field data of obtained from Tabas coal mine in Iran. [Copyright &y& Elsevier]

Abstract: The problem of Tunnel Boring Machine (TBM) selection has a significant impact on the speed and cost of excavating sector; so that it is a strategic issue. On the other hand, selecting the optimum TBM among a pool of alternatives is a multicriteria decision making (MCDM) problem. In this paper, an evaluation model based on the fuzzy analytic hierarchy process (AHP) and another fuzzy MCDM technique, namely fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is developed to help the tunneling designers in the process of the TBM selection under fuzzy environment where the vagueness and uncertainty are taken into account with linguistic variables parameterized by triangular fuzzy numbers. The fuzzy AHP is applied to form the structure of the TBM selection problem and to determine weights of the evaluation criteria, and fuzzy TOPSIS method is utilized to acquire final ranking. A real world case study is illustrated in order to demonstrate the potential of the proposed model for the TBM selection issue. It demonstrates the effectiveness and capability of the proposed model. [Copyright &y& Elsevier]