Bibliographic record
Abstract
Southwestern British Columbia is exposed to the highest seismic risk in Canada. Estimation of the damage to structures as a consequence of a major earthquake is essential for emergency and risk management. In this thesis, first the probability and distribution of seismic damage to structural and non-structural components of buildings were calculated for three cities in southwestern BC, Vancouver, Victoria and New Westminster, using conventional damage estimation procedures. This involved estimation of the seismic hazard in the area, compilation of building databases for these cities, and the calculation and mapping of the damage distribution using Geographic Information Systems. The building classification scheme with 31 prototypes and the damage matrices relating the expected level of damage for each of these prototypes at different levels of ground shaking were available to be used in these analyses. Among the three case studies, the highest risk was observed in the City of Victoria, in which about 35% of the blocks within the study area were estimated to have damage levels between 10% and 30% of the replacement cost. The estimated damage level in Vancouver stayed generally in the 5% to 10% range, however, it went up to the 10%- 20% range in quite a few number of blocks. About half of Victoria lies on relatively soft soil that is expected to amplify the peak ground accelerations by about 1.5 (if the earthquake dominantly contains low frequencies), which in turn increases the expected damage up to higher than 30% in many blocks. Direct monetary losses due to structural and non-structural damage to buildings were also estimated following the damage assessment. Next, an alternative damage estimation method was investigated and the necessary parameters to apply this method to buildings constructed in BC were developed for three sample building types. The damage levels obtained from the two methods were compared and differences were discussed.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".