Use of the ranking distance as an index for assessing the accuracy and precision of equations for the bearing capacity of piles and at-rest earth pressure coefficient
Bibliographic record
Abstract
In many geotechnical design situations, a number of different calculation models have been developed to predict the value of a particular quantity required for use in design calculations, for example, the bearing capacities of driven and root piles, and K0, the at-rest earth pressure coefficient. In this paper the authors show how the dependability of different calculation methods can be compared and assessed using a synthetic probabilistic approach and the ranking distance (RD) index. Measured values, Qmeas, are compared with calculated values, Qcalc, using the "bias factor," defined as the ratio Qmeas/Qcalc. The bias factor values obtained using a particular calculation method are processed to evaluate the "accuracy" and "precision" by calculating a central tendency and a variability statistical parameter, respectively, from the values. The RD index is a comprehensive statistical parameter for assessing the dependability of a particular calculation method and is based on the central tendency and variability. Using the ratios between calculated and measured bearing capacity and earth pressures values, the RD index is used to assess the accuracy and precision of the most frequently used pile driving formulae, two equations for the bearing capacity of root piles, and seven equations for the at-rest earth pressure coefficient.Key words: accuracy, precision, probabilistic approach, ranking distance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".