Methodology for the seismic risk assessment of low-rise school buildings in British Columbia
Bibliographic record
Abstract
This thesis presents a methodology for the seismic risk assessment and risk reduction of schools in British Columbia. The methodology permits school buildings to be ranked by risk levels, and includes information that allows designers to establish the seismic capacity of school buildings and to select appropriate retrofit options. This research includes the treatment of seismic hazard in the province by reference to different types of earthquakes that affect the region, and the development of an extensive database of structural performance of typical school buildings for different types of earthquakes and levels of shaking. The seismic hazard in the province is due to crustal, subcrustal and subduction earthquakes. The ground motion characteristics and the rates of occurrences of these different types of earthquakes are sufficiently different that it justifies assessing their effects separately in the risk calculations. The results of probabilistic seismic hazard analyzes have been combined with incremental nonlinear dynamic analyzes of a variety of structural systems subjected to the three earthquake types. A suite of thirty ground motions representative of these earthquakes has been used for the calculation of seismic risk. This process resulted in a large database of response of structural systems on different types of soils. The database was developed first for systems on firm soils (Site Class C). To account for soft soils (Site Class D) a simplified procedure was developed to convert structural performance on Class C sites to that on Class D sites. This thesis presents information that contributes to the state of knowledge in seismic risk in two forms: research and engineering practice. It provides a better understanding of how the risk in a region can be deaggregated according to the earthquake types, how representative ground motions for each earthquake type can be selected, and how the site conditions can be incorporated in probabilistic risk assessment. The contribution to engineering practice is the development of a ready-to-use methodology for risk assessment and for determining whether or not a retrofit is required for a giving type of structure on a certain type of soil and in a given seismic region.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".