Reduced-scale Testing of Masonry Structures to Explosions
Bibliographic record
Abstract
In recent decades, the number of historical and ancient structures exposed to blast loads has steadily increased, either due to accidental or deliberate explosions, such as the archaeological site of Palmyra in 2015 and Beirut explosion in 2020.It is important to protect such assets against blast loading.However, investigating how structures respond to explosions cannot rely solely on numerical and analytical tools.Experimental tests are necessary to enhance our current understanding and validate existing models.Large-scale experiments can only be conducted in specialized testing areas with restricted access, safety concerns, and limited repeatability.An alternative approach for studying the effects of blast loads on structures is to rely on reduced-scale experiments in laboratory conditions.Reduced-scale experiments offer a high level of repeatability, moderate cost, and reduced hazards associated with environmental safety.Presented here is a new design setup for studying masonry assets based on reduced-scale experiments for the rigid-body response of structures.Blast waves and loading are emulated by detonating wires triggered by high-voltage discharges from a capacitor.These experiments take place within a controlled laboratory environment, ensuring both repeatability and safety.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".