Seismic Performance of Currently Designed Automated Rack Supported Warehouses
Bibliographic record
Abstract
The paper analyses the seismic performance of Automated Rack Supported Warehouses (ARSWs), huge steel buildings developed for an optimized storage and management of palletized goods.These buildings have a load-bearing structure made up of steel racks that have the same peculiar characteristic of traditional steel racks, but with the relevant difference that the formers are demanded to resist to all the types of loads (e.g., wind, snow, earthquakes, gravity loads) while the latter to only the storage loads and the eventual associated inertial forces in case of earthquakes.Despite this relevant difference, the ARSWs have inherited almost all the structural characteristics of the traditional steel racks, resulting often in a non-satisfactory structural behaviour, as highlighted by recent collapses of ARSWs after seismic events.With the aim of assessing the seismic behaviour of ARSWs designed following the current guidelines, the paper presents 5 ARSW structures designed from 5 big European companies, analysing the different design approaches and hypothesis, and the consequences of these different choices in terms of seismic demand and performance.The results presented point out that a proper and dedicated design approach is needed for ARSWs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".