Unsupervised machine learning for data-driven classification of rock mass using drilling data: How can a data-driven system handle limitations in existing rock mass classification systems?

• URL: http://arxiv.org/abs/2405.02631v1
• Date: Sat, 4 May 2024 10:54:07 GMT
• Title: Unsupervised machine learning for data-driven classification of rock mass using drilling data: How can a data-driven system handle limitations in existing rock mass classification systems?
• Authors: T. F. Hansen, A. Aarset,
• Abstract summary: Rock mass classification systems are crucial for assessing stability and risk in underground construction globally. Rock mass classification systems developed in the 1970s lack access to modern high-resolution data and advanced statistical techniques. We show that it is possible to form well-defined clusters that can act as a foundational basis for various rock mass classification systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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