Analyzing Drill Core Logging Using Rock Quality Designation–60 Years’ Experience from Modifications to Applications
Bibliographic record
Abstract
The accurate analysis of rock cores is of primary importance for designing in and on the rock mass environment. There are several methods for analyzing boreholes, but the most accepted and widely used method is the rock quality designation (RQD) value, which has been a core rating metric for six decades. The RQD value serves as: (1) an important input parameter for rock mass classifications such as RMR and Q; (2) a basis for calculating the Geological Strength Index (GSI) of boreholes; and (3) a key indicator in assessing rock mass quality, particularly in highly fractured or weak rock masses. The original RQD method has several drawbacks and shortcomings, which have led to numerous proposed amendments. This review paper aims to: (1) summarize alternative methods of calculating the RQD value; (2) analyze the sensitivity of different rock mass classifications to the accuracy of this value; and (3) present a systematic analysis of the practical implications of modified RQD methods, emphasizing advancements such as DFN modeling, seismic RQD techniques, and machine learning-based approaches. The findings provide a comprehensive framework for more robust and versatile assessments of rock mass quality.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".