Current rock engineering design in drill and blast tunnelling primarily
relies on engineers' observational assessments. Measure While Drilling (MWD)
data, a high-resolution sensor dataset collected during tunnel excavation, is
underutilised, mainly serving for geological visualisation. This study aims to
automate the translation of MWD data into actionable metrics for rock
engineering. It seeks to link data to specific engineering actions, thus
providing critical decision support for geological challenges ahead of the
tunnel face. Leveraging a large and geologically diverse dataset of 500,000
drillholes from 15 tunnels, the research introduces models for accurate rock
mass quality classification in a real-world tunnelling context. Both
conventional machine learning and image-based deep learning are explored to
classify MWD data into Q-classes and Q-values, examples of metrics describing
the stability of the rock mass, using both tabular and image data. The results
indicate that the K-nearest neighbours algorithm in an ensemble with tree-based
models using tabular data, effectively classifies rock mass quality. It
achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass
into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification
with E versus the rest. Classification using a CNN with MWD-images for each
blasting round resulted in a balanced accuracy of 0.82 for binary
classification. Regressing the Q-value from tabular MWD-data achieved
cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model
as in classification. High performance in regression and classification boosts
confidence in automated rock mass assessment. Applying advanced modelling on a
unique dataset demonstrates MWD data's value in improving rock mass
classification accuracy and advancing data-driven rock engineering design,
reducing manual intervention.

通过自动将 MWD 数据转换为可操作的岩石工程指标，本研究利用大规模且地质多样化的数据集，探索传统机器学习和基于图像的深度学习方法，在真实的隧道工程环境中实现准确的岩体质量分类，提供决策支持及降低人工干预。