TY - DATA T1 - Python notebooks underlying the MSc thesis: Unveiling Creep Phenomena and Pillar Stability in Valkenburg Limestone Mines: A Holistic Approach PY - 2024/11/06 AU - Nikolaos Antoniadis UR - DO - 10.4121/f44790eb-f97d-4f7c-964e-3ed2fb3d3062.v1 KW - Creep KW - Fibre Optics KW - Abandoned Mines KW - Room and Pillar KW - Calcarenite KW - Machine Learning KW - LSTM KW - ARIMA KW - Long short-term memory KW - autoregressive integrated moving average N2 -

The accompanying Python notebooks were developed for the Master’s thesis titled "Unveiling Creep Phenomena and Pillar Stability in Valkenburg Limestone Mines: A Holistic Approach." This research aimed to understand the long-term creep deformation and stability of calcarenite pillars in the Valkenburg limestone mines. In the notebooks, environmental and structural data, such as temperature, precipitation, and pillar dimensions, which were collected via an advanced fibre-optic monitoring system are taken into account. In the notebooks, LSTM and ARIMA models are implemented to capture the complex, non-linear interactions between the examined factors and the pillar behaviour over time. Additionally, a separate notebook contains calculations for correlation analysis and safety factors calculations, contributing to a detailed assessment of the conditions impacting pillar stability.

ER -