Méthodologie pour la sélection et l'étalonnage d'accélérogrammes pour l'analyse sismique non linéaire de bâtiments localisés dans l'est de l'Amérique du Nord
Bibliographic record
Abstract
This thesis presents comparisons of different scaling and spectral matching methods for ground motions to perform nonlinear dynamic analysis.This is done to determine (a) which variation of magnitudes could be obtained from one method to the other, (b) identify the advantages and disadvantages of the different methods and (c) recommend which methods should be used to scale or modify ground motions.Eight of these methods are applied to a group of 30 historical ground motions.They are then used for nonlinear dynamic analyses on single degree of freedom structures, a 10-storey reinforced concrete shear wall and a 4-storey steel braced frame structure located in Montreal.A secondary objective is to determine if groups of 1, 3 or 7 ground motions should be used for nonlinear dynamic analyses.The scaling methods recommended after these analyses are the SIa and MSE scaling methods.The SIa method consists in measuring the area under the response spectra which means between two preselected periods as well as the area under the UHS between these same two periods.The scaling factor is computed to have these areas equal to each other.The MSE method aims at minimising the mean square error between the UHS and the response spectra.These methods show more realistic structural responses with all the structures as well as a low standard deviation.The frequency domain (FD) and time domain (TD) spectral matching methods return standard deviations significantly higher than the ones obtained with the MSE and SIa methods thus they are not recommended for practical use in nonlinear analysis.To select the appropriate number of accelerograms required for adequate dynamic analysis, results from groups of 1, 3 and 7 ground motions have been compared.It is concluded that, in general, a group of 7 ground motions allows lower seismic forces because this group is allowed to use the average structural response.In some situations, a group of 3 ground motions can return not conservative structural response as well as, in other cases, too conservative structural response.Groups of 1 accelerogram are not recommended because, even with spectral matching methods (FD and TD), the standard deviation is too high to obtain representative results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".