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MECHANISED TUNNELLING | TECHNICAL


Table 6: Further details of the dataset used in models Rock Type Code


Training N


Class G & GN UCS (MPa) Jv


FPI (kN/cutter/mm/rev)


Class MV UCS (MPa) RQD (%)


FPI (kN/cutter/mm/rev)


Class SLK UCS (MPa) RQD (%)


FPI (kN/cutter/mm/rev) Class C


UCS (MPa) RQD (%)


FPI (kN/cutter/mm/rev)


220 220 220


120 120 120


118 118 118


110 110 110


Min


38.3 0.2


14.01


16 10


5.9


20 30


9.52 6 10 2.75 Max


267.9 29.3


161.25


226.4 100


70.68


176 100


45.44


105 85


19.91 Mean


146.05 8.39


47.34 92.98


67.065 21.69


74.54 70.16 19.1


40.92 49.33 11.39


Test N


38 38 38


23 23 23


21 21 21


16 16 16


Min 82 0.8 18.4


40 20


6.69 40


40.75 9.79


6


22.5 3.7


Max


246 26.7


145.6


227.4 100 53.6


175 100


43.15


65 90


18.02


Mean


171.65 8.91


63.46


117.56 76.07 25.44


101.67 75.67 21.22


36.84 53.12 9.43


there was availability of Jv data, which showed better correlation with FPI compared to RQD. This could be attributed to RQD being limited in representing the degree of fracturing in hard massive rocks, since it is an index with the maximum value of 100 which indicates the discontinuity spacing/frequency. Perhaps this is why Gong and Zhao (2009) and Delisio and Zhao (2014) considered Jv to be a better representative of joint frequency in their developed models (Salimi 2021). To develop and evaluate the empirical models,


the data were divided into two categories – training, and testing/validation. Many investigations have recommended 20% of data for testing procedure. However, by partitioning the available data into 80% training (and so 20% for testing), it can be expected that the number of samples for use in learning the model is drastically reduced, and results can depend on a particular random choice for the pair of sets (train, validation). To address this issue, between 15%-20% of the data were used for testing and validating the models. Further details of training of the models is presented in Table 6. The analysis of available input parameters for each


rock type, using R statistical computing software (R stats, car, and MASS packages as well as caret library in R) yielded the following empirical equations. The comparison between the calculated and predicted FPI for each rock type is shown in Figure. 3.


2 3


4 5


Rock type “G&GN” : FPI = e2.775 . UCS0.367 . Jv -0.432


Rock type “MV” : FPI = exp(1.633 + 0.007 . UCS +0.009 . RQD (R2


(R2 = 0.71)


Rock type “SLK” : FPI = exp(2.164 + 0.004 . UCS +0.006 . RQD (R2


Rock type “C” : FPI = e-0.822 . UCS0.146 . RQD0.69 = 0.73) (R2 = 0.72)


Above, figure 3: Comparison between the calculated and predicted FPI based on rock type categorisation via regression analysis for training and testing datasets


July 2024 | 21 = 0.70)


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