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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: Roof fall is one of the most important hazards connected with underground coal mines, because significantly affects financial and human losses. On the other hand, according to different parameters make a significant impact on roof fall and these factors are ill-defined or even immeasurable, this problem is an uncertain and complex issue. Fuzzy logic is a useful tool to handle the existing uncertainty to be more adapted to the real world problems. In this paper, fuzzy inference system (FIS) is applied to predict roof fall rate more accurate, precise, and sure for controlling, mitigating, and/or even eliminating the risk of roof fall. The model utilizes subtractive clustering method to generate fuzzy rules based on 109 data of roof performance from US coal mines. The established model is evaluated by testing dataset based on three indices, including coefficient of determination (R 2), mean absolute error (MAE), and root mean square error (RMSE). The results derived from the FIS model in comparison with artificial neural network (ANN) and multivariate regression (MVR) model demonstrate that prediction of roof fall rate by the FIS model is more accurate and satisfied. [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]

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage. [ABSTRACT FROM AUTHOR]