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Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems. [ABSTRACT FROM AUTHOR]

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]

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]