Stability of weak rock slopes are threatened by structurally controlled mechanisms or mass failure. Shallow depth lignite beds are commonly exploited by surface mining. Preliminary slope stability analysis is carried out by practical tools that perform quick stability investigation with limited data. Kinematical analysis is a widely used method that requires orientational data and the rock mass friction angle. However, geological or mechanical features of the rock mass are disregarded. Slope Mass Rating (SMR) is an alternative that considers the basic Rock Mass Rating (RMR) parameters and the discontinuity orientations. However, it suffers from the common drawbacks of classification systems. In addition, it is required to be tuned for weak rock mass. This study modifies the classical SMR system by Fuzzy Logic to predict the slope failure mode. Enhancements on the prediction mechanism depend on the expert opinion. Fuzzy membership functions and rules were outlined by the expert view. The fuzzy interpretation mechanism was established. Later, the modified methodology was validated on two large slope failures of surface lignite mines in Turkey. Other failure modes were investigated by 3D discontinuum numerical simulations. The modified methodology presented in this study enhances the prediction capability of SMR with no claim to replace the analytical or numerical solutions.

1 Introduction

Slope stability is the key to sustainable production in surface mining. In the early design stage, the safe slope is outlined by preliminary analyses and investigated in detail by numerical simulations. On the other hand, slope monitoring is performed to ensure after mining stability. Overall slope angle is the dominant parameter in slope stability. Although numerical analysis is potentially the most reliable design procedure, costly and exhaustive experimental programs diminish the advantages of this method. Practical tools are required to obtain quick and safe slope designs with easily collectible data.

Slope performance charts carry out preliminary slope design based on slope height and rockmass quality. Unfortunately, they do not consider any instability due to structural geology. The kinematical analysis is a handy tool to asses failure modes based on slope and discontinuity orientations. However, geomechanical features of rockmass are not distinguished, and it lacks predicting slope mass failures. Slope quality ratings have been established to predict potential slope failure modes. Slope Mass Rating (SMR) (Romana, 1985) is one of the popular slope quality classification systems, and it is a modification of well-known Rock Mass Rating (RMR) (Bieniawski, 1973). Classification systems suffer from ambiguities as a result of defining parameter ratings in terms of linguistic statements. Basarir and Saiang (2013) proved that the classical rating scheme might assign the same quality class for different rock mass properties. Fuzzy Logic transforms verbal statements into quantitative data to better interpret the intermediate states.

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