Seismic Design and Response of Crane-Supporting and Heavy Industrial Steel Structures
Bibliographic record
Abstract
This paper presents an analytical study of the seismic behavior of two different types of industrial buildings; a regular mill-type crane-supporting steel structure and an irregular heavy industrial building housing a vertical mechanical process. For both structures, the seismic response is examined through elastic time-history dynamic analyses in order to validate the predictions from the equivalent static force procedure and the response spectrum analysis method prescribed in current building codes. The analyses also serve to assess the inelastic demand in crane-supporting structure. For the crane-supporting structure, analyses are performed for sites in Montreal and Vancouver in Canada and in Seattle in the United States. The results show that the median horizontal displacement and acceleration from the time-history analyses are generally well predicted by the code analysis methods. Inelastic response in these buildings is likely to develop in the form of buckling of the lower column segment, a failure mode that exhibits limited ductility. For the tall irregular building, the analyses are performed for the Montreal site only. The results show the equivalent static method provides fair displacements estimate, but may lead to unconservative predictions of column and brace forces. Response spectrum analysis method, as prescribed in design codes, appears to provide appropriate prediction of the seismic response of such highly irregular structures. For both building types, a good prediction from response spectrum and time-history analysis methods is possible only when a sufficient number of modes are used.
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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 | 0.000 | 0.001 |
| 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".