Analysis of a damaged 12-storey frame-wall concrete building during the 2010 Haiti earthquake — Part II: Nonlinear numerical simulations
Bibliographic record
Abstract
After the 2010 Haiti earthquake, which destroyed a significant part of the seismically vulnerable city of Port-au-Prince, the country’s capital, a 12-storey reinforced concrete building that behaved well was investigated to understand its dynamic response. This paper completes the experimental work presented in a companion paper, in which the dynamic properties of the building were obtained from ambient vibration tests, and from which a finite-element model was updated. This paper’s main objectives are: (i) to understand the causes that led to the observed structural damage; and (ii) to estimate the likely seismic excitation at the site of the building. Several nonlinear analyses involving various ground motion intensities were conducted and the results were compared with the damage reported during the on-site survey. The numerical models reproduced the observed damages well and helped to explain them. The overall response of the mixed stiff frame–wall structure was clearly dominated by the high stiffness of the shear walls, showing that this type of structural system helps in keeping reasonable interstorey drift levels. Overall, the building’s structure seems to have responded linearly to all the ground motions investigated, but deformation demands imposed to the frame by the shear walls lead to local damages.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".