Seismic response of tall buildings using ground motions based on National Building Code Canada 2015
Bibliographic record
Abstract
The seismic analysis of tall buildings requires nonlinear analysis in order to determine their behaviour in a more realistic manner. Nonlinear analysis necessitates the input of suitable ground motions records that represent the hazard at the site of the building. Finding appropriate ground motions is an arduous task, mainly because there are not enough records that are compatible with the hazard level prescribed in the codes for the site of the building. Therefore, existing records must be modified somehow to match the target hazard. The National Building Code of Canada (NBCC) provides guidelines for selecting and scaling ground motions to a target spectrum. This research includes the nonlinear seismic evaluation of a 44-storey concrete building. The structure resembles the characteristics of a typical high-rise in downtown Vancouver. A Probabilistic Seismic Hazard Analysis (PSHA) was performed to determine the governing sources of the site. These seismic sources include crustal, subcrustal and subduction ground motions. The selection and scaling for the three types of earthquakes (crustal, subcrustal and subduction) was performed per the National Building Code Canada 2015. The input of ground motions consisted of 33 pairs of records, 11 of each source. Spectral matching techniques were also employed to match the ground motions to the target spectrum, and the responses between both scaling procedures were compared. The results showed that the subduction records mainly governed the responses of the building. But the responses from the crustal and subcrustal records were also significant and cannot be discarded. It was observed that spectral matching and the code based scaling procedure generated similar responses. In addition, issues with the Code based scaling procedure were addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".