  Rock mass classification systems are crucial for assessing stability and risk
in underground construction globally and guiding support and excavation design.
However, systems developed primarily in the 1970s lack access to modern
high-resolution data and advanced statistical techniques, limiting their
effectiveness as decision-support systems. Initially, we outline the
limitations observed in this context and later describe how a data-driven
system, based on drilling data as detailed in this study, can overcome these
limitations. Using extracted statistical information from thousands of MWD-data
values in one-meter sections of a full tunnel profile, thus working as a
signature of the rock mass, we have demonstrated that it is possible to form
well-defined clusters that can act as a foundational basis for various rock
mass classification systems. We reduced the dimensionality of 48-value vectors
using nonlinear manifold learning techniques (UMAP) and linear principal
component analysis (PCA) to enhance clustering. Unsupervised machine learning
methods (HDBSCAN, Agglomerative Clustering, K-means) were employed to cluster
the data, with hyperparameters optimised through multi-objective Bayesian
optimisation for effective clustering. Using domain knowledge, we experienced
improved clustering and system tuning opportunities in adding extra features to
core clusters of MWD-data. We structured and correlated these clusters with
physical rock mass properties, including labels of rock type and rock quality,
and analysed cumulative distributions of key MWD-parameters for rock mass
assessment to determine if clusters meaningfully differentiate rock masses. The
ability of MWD data to form distinct rock mass clusters suggests substantial
potential for future classification systems grounded in this objective,
data-driven methodology, free from human bias.
