Method for Quantification of Wear of Sheared Joint Walls Based on Surface Morphology
Bibliographic record
Abstract
Roughness and wear evolution of three different joint wall surfaces were characterized using surface roughness and surface wear parameters. Parameters were defined by considering the two components of morphology: waviness (“primary” roughness) and surface roughness (“secondary” roughness). Two surface roughness parameters are proposed: joint interface (or single wall) specific surface roughness coefficient SR s (0 ≤ SR s ≤ 1) for quantifying the amount of “pure” roughness (or specific roughness), and degree of joint interface (or single wall) relative surface roughness DR r (0 ≤ DR r ≤ 0.5). Two further parameters are also proposed in order to quantify the wear of wall surface: joint interface (or single wall) surface wear coefficient Λinterface, and the degree of joint interface (or single wall) surface wear D w(interface). The three test specimens were: man-made granite joints with hammered surfaces, man-made mortar joints with corrugated surfaces, and mortar joints prepared from natural rough and undulated schist joint replicas. Shearing under monotonic and cyclic shearing was performed using a computer-controlled bidirectional and biaxial shear apparatus. Joint surface data were measured using a noncontact laser sensor profilometer prior to and after each shear test. Calculation of specific surface roughness coefficient SR s , and degree of surface wear D w , indicated that the hammered joint interface with predominant interlocking wears much more (>90%) than the corrugated (27%) and the rough and undulated (23%) joint interfaces having localized interlocking points. The proposed method was also successfully linked to the classical wear theory.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".