Crack assessment of RC beam–column joints subjected to cyclic lateral loading using acoustic emission (AE): the influence of shear links aspect
Bibliographic record
Abstract
Existing reinforced concrete (RC) beam–column joints, which are designed mainly to resist gravity load, may encounter serious problems in low to moderate seismic regions. The moderate earthquake in Ranau, Malaysia, affected numerous old buildings, mainly manifesting as uncontrolled crack propagation in RC members at the beam–column joint. Effects of shear reinforcement between shear link spacing on crack formation on four RC beam–column joints were investigated under cyclic lateral loading with acoustic emission (AE) monitoring. The control specimen (BCJ-1), which was designed in BS-8110, showed more irregular cracks thus concrete cover spall was observed at an earlier drift ratio (DR) level than for other specimens with additional shear reinforcement between shear link spacing. Sudden drop of AE hits beyond 2.25% DR level with a significant increase in crack width showed that BCJ-1 was no longer able to resist higher loads due to de-bonding between reinforcement and concrete. From the rise time/amplitude against average frequency (RA-AF) graphs, the mode of failure was determined according to DR stages concerning crack characteristics.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".