Data preparation for machine learning in rock engineering
Bibliographic record
Abstract
Abstract Digitalization in rock engineering has resulted in significant technological advancements and the increasing use of machine learning techniques. As rock engineering transitions into becoming more data-driven, machine learning can help rock engineers improve the efficiency and utilization of large data sets in the design process. While machine learning is a powerful tool, the success of machine learning algorithms is intrinsically related to the quality and quantity of data available. It is commonly accepted that machine learning algorithms that are trained on poor quality data will result in poor and inaccurate (i.e. highly subjective) results. To limit the human factors that result from using data that represent qualitative assessments rather than objective measurements of physical properties, it is imperative to improve the data analysis and preparation/labelling process. Data preparation is especially important when applying machine learning to rock engineering problems due to the inductive and empirical nature of the design process as a result of the inherent variability of geological materials. Despite data preparation accounting for more than half of the machine learning process, there is limited research on data preparation for machine learning in rock engineering. This paper aims to fill this gap by providing a set of guidelines on the necessary data preparation steps for applying machine learning to rock engineering problems, thereby helping rock engineers improve the performance of their machine learning models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".