Automated Analysis And Performance Evaluation Of Earthquake Resistant Steel Frame Buildings
Bibliographic record
Abstract
Special purpose structural analysis programs often lack the user interfaces and the flexibility the way modern software offers in terms of dynamic libraries and Application Programming Interfaces. This paper discusses the development of a pre and po st processing tool for DRAIN-2DX, a program for seismic response a nalysis of building frames. The paper f ocuses on the e arthquake resistant design of steel frame buildings. The tool developed here is used for automating the dynamic analysis process such that a large scale simulation for the evaluation of seismic performance of steel frame buildings could be carried out easily. Such large scale simulations produce a huge amount of data which would be handled by the post-processor in order to extract meaningful information about t he behaviour of a building under earthquakes. As a case study, a ten storey moment resisting steel frame building designed b ased on the NBCC 2005 seismic provisions are analyzed using the software tools discussed here. The building is assumed to b e located in Vancouver ion western Canada. For earthquake resistant design, evaluation o f the seismic performance of buildings is essential t o determine if an acceptable solution in terms of performance is achieved. The seismic performance of the buildings has been evaluated u sing nonlinear static a nd dynamic response history analysis. A set of eight simulated ground motion records that are c ompatible with the seismic hazard spectrum of Vancouver have been used in the dynamic analysis. Seismic excitation causes damage in a structure, which is reflected in the response of the structure. The results are discussed in d etail t o demonstrate the a dvantages of the tools developed herein.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".