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At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends. Full article

Empirical systems for the classification of rock mass are used primarily for preliminary support design in tunneling. When applying the existing acceptable international systems for tunnel preliminary supports in high-stress environments, the tunneling quality index (Q) and the rock mass index (RMi) systems that are preferred over geomechanical classification due to the stress characterization parameters that are incorporated into the two systems. However, these two systems are not appropriate when applied in a location where the rock is jointed and experiencing high stresses. This paper empirically extends the application of the two systems to tunnel support design in excavations in such locations. Here, the rock mass characterizations and installed support data of six tunnel projects are used. The back-calculation approach is used to determine the Q value using the Q-system support chart, and these values are then used to develop the equations and charts to characterize the stress reduction factor (SRF), which is also numerically evaluated. These equations and charts reveal that the SRF is a function of relative block size, strength–stress ratio, and intact rock compressive strength. Furthermore, the RMi-suggested supports were heavier than the actual installed ones. If the approximate inverse relation between stress level (SL) and SRF is used, the difference between the actual and the recommended supports increases when using the RMi-recommended rock support chart for blocky ground. An alternate system is made for support recommendation using a Q-system support chart. In this system, the ground condition factor is modified from the available parameters, and a correlation is developed with a modified Q system. Full article