Mitigation of blast effects through novel energy-dissipating connectors
Bibliographic record
Abstract
Explosions generate overpressures that can cause irreparable damage to structures. For many buildings, especially critical infrastructure, continued operation after an explosive attack is essential. The use of energy-dissipating methods will enable the protection of a structure and occupants from a blast and permit the timely repair and re-occupation of the building after an event. The concept behind the system presented is the creation of panels that can be used as cladding for structures. The panels are connected to the main structure using energy-dissipating component assemblies around the panel edge. When subjected to a blast load the panels transfer the blast pressure through the assemblies, thereby reducing the forces transmitted to the underlying structure. After an event, the panels and energy-dissipating component assemblies can be replaced quickly and easily, allowing the building to be reoccupied in a short time after an attack. This study focuses on the characterization of energy-dissipating component assemblies using static and dynamic laboratory testing. A predictive theory, supported by a single degree of freedom model, is developed and a general evaluation method proposed. Further laboratory testing expands the characterization of behaviour of the assemblies through experiments, with a blast generator in tension tests and in simulated blast panel tests. The time histories developed from tension tests are then compared to examine the effect of loading rate. The investigations on blast panels also include a comparison with predictions to determine whether the latter can describe the global behaviour of the system. Lastly, the response of the energy-dissipating component assemblies is evaluated in full-scale field blast tests on cladding panels.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".