MECHANISED TUNNELLING | TECHNICAL


Left, figure 1:


Distribution curve and frequency histogram of rock mass and TBM performance parameters in the database grouped by rock type (JV


is only


available for massive hard rocks)


Usually, stronger and less fractured rock masses are


more difficult for disc cutters and, therefore, boring by TBM requires higher thrust levels to achieve a certain depth of penetration. Therefore, higher values of FPI are usually seen in strong and massive rock masses. In contrast, there is no need to apply high thrust values for excavation of poor-quality rock masses (weaker and more fractured) due to crack initiation and propagation being enhanced by pre-existing fractures. FPI values are low in such conditions. The graphs in Figure. 1 show the histograms and


distribution curves of different geological and TBM performance parameters from the database for different rock types. The joint volumetric count (Jv) was not available for all selected tunnel sections.


DEVELOPING NEW MODELS In rock engineering practice, statistically-based empirical equations have been extensively applied to predict target variables based on other operational or geological parameters. Such equations have great importance during the early stages of rock excavation and design works since they are more practical compared to extensive theoretical analyses. In the field of geomechanics, each rock type has its


own texture, grain size, cementation and behaviour that affect the boreability and penetration rate of TBMs. Salimi et al. (2019a) considers Rock Type Code (RTC) as an input parameter in the proposed model to estimate the FPI. Laughton introduced RTC in 1990’s and it has been used by Farrokh et al. (2012).


Table 3 displays seven rock type categorisations


– with four classes for sedimentary, and the others are metamorphic, granitic, and volcanic, respectively. It should be noted that gneiss (GN) is inherently metamorphic but is typically closer to granite in behaviour, especially where foliation is less pronounced. For this reason, it was categorised as GN in the analysis. Rock type code was used as input parameters to the model, with code numbers including 1 for both G and GN, 2 for MV, 3 for SLK, and 5 for C (Salimi et al. 2019a, b).


According to the results of sensitivity analysis and


parametric study of common models (Fatemi et al., 2016), consideration of RTC has a significant role to play in estimation of rock mass boreability. The same results have been found by Salimi (2021) and Salimi et al. (2019a). Generally speaking, rock texture (grains and matrix) directly correlated with the physical and mechanical properties of rock material and thus rock drillability.


Table 3: Rock type categorisation in database Rock type


Claystone, mudstone, marl, slate, phyllite, argillite Sandstone, siltstone, conglomerate, quartzite Limestone, chalk, dolomite, marble Karstic Limestone


Metamorphic rocks such as gneiss and schist Coarse igneous such as granite and diorite Fine volcanic such as basalt, tuff, and andesite


Code C S L K


M G V


July 2024 | 19


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