The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/f44790eb-f97d-4f7c-964e-3ed2fb3d3062

Antoniadis, Nikolaos (2024): Python notebooks underlying the MSc thesis: Unveiling Creep Phenomena and Pillar Stability in Valkenburg Limestone Mines: A Holistic Approach. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f44790eb-f97d-4f7c-964e-3ed2fb3d3062.v1

Dataset

• Civil Engineering
• Geology
• Heritage
• Earth Sciences
• Engineering
• Cultural Understanding

Abandoned Mines, ARIMA, autoregressive integrated moving average, Calcarenite, Creep, Fibre Optics, Long short-term memory, LSTM, Machine Learning, Room and Pillar

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Nikolaos Antoniadis

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.

• 2024-11-06 first online, published, posted

4TU.ResearchData

ipynb

• https://repository.tudelft.nl/record/uuid:cd18da61-3142-4958-8d57-56df2c796c31

TU Delft, Faculty of Civil Engineering and Geosciences