RBF composite parameter model for structural surface roughness with factor analysis
Bibliographic record
Abstract
The characterization of the structural surface roughness is the groundwork for predicting the peak shear strength. The single parameter cannot fully reflect the characteristics of structural surface morphology. The index system composed of some single characterization parameters is a nonlinear system with much overlapping information. The factor analysis is conducted to reduce the dimension and strip the overlapping information by canonical normalization. At the same time, the standardized index system is transformed from nonlinear into linear by the RBF neural network structure. In practice, 6 statistical parameters reflecting the structural surface roughness are selected, the inverse calculation of JRC is established, 76 training samples and 37 group test samples are built. The multi-index composite parameters are established considering the characteristics of embossment of joints, such as height, angle and contact degree. In the mean time, the number of neurons in the hidden layer is fixed to improve the calculation speed. The prediction accuracy of the model is verified by the sample data and the direct shear tests of rock joints. The relative error and determination coefficient are calculated through the measured data to evaluate the performance. Finally, the applicability of the factor analysis and possible error analysis are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".