Using BIM for the Assessment of the Seismic Performance of Educational Buildings
Bibliographic record
Abstract
The progress of the study of seismic vulnerability has allowed the formulation of new assessment methodologies, which take into account not only the behaviour of the structural and non-structural elements, but also the components that, due to their importance and cost, can represent an investment that in some cases becomes greater than the cost of the whole building.To carry out this more specific type of study, it is necessary to use tools that allow estimating, locating and properly characterizing the components, which has been a problem that has not yet been solved, due to the inability to maintain together all the components in a single model of a building.This paper presents the results of a research in which BIM procedures have been combined to overcome these deficiencies, successfully implementing it in the assessment of the seismic vulnerability of a set of university buildings which have been built in the middle of 1970's and 2000's, improving the quality of the information necessary to perform the numerical simulations and the consequent quantification of the damage that allowed obtaining the required repair costs, under the scenario of the occurrence of a maximum probable earthquake.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".