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?

arXiv:2405.02631v1 Announce Type: new
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 …

abstract access arxiv classification construction cs.et cs.lg cs.sy data data-driven design eess.sy however limitations machine machine learning risk stability support systems type unsupervised unsupervised machine learning