GIS-Based Seismic Damage Estimation: Case Study for the City of Kelowna, BC
Bibliographic record
Abstract
This study integrates risk assessment tools for diagnosis of urban areas against seismic disasters (RADIUS) and geographic information system (GIS), hence forth denoted as GBR (GIS based RADIUS). The GBR is applied for seismic damage estimation of city of Kelowna, in the interior of British Columbia, Canada. Ground-shaking intensity in the area was developed utilizing the seismic source zones defined by the Geological Survey of Canada and opinions from the local experts. Building inventories were compiled by aggregating data from municipal databases as well as sidewalk surveys and surveys through Google Maps. The GIS tool came in to be handy to provide a basis for effective decision making and gauge the vulnerable areas. Estimated damage and damage distributions were mapped on a block-by-block (5×5 km) basis. The assessment revealed that an earthquake scenario of M8.5 in the Cascadia Zone may potentially damage around 58 buildings within the city, causing 12 injuries. Plus, the study showed some damage assessment for the lifelines, for example, road and water pipelines networks. The assessment results further revealed that the city of Kelowna downtown area was expected to suffer the highest amount of damage, which in turn may produce the highest amount of economic loss, because it is the concentration of concrete high-rise buildings and clustered economic activities. Therefore, for good measure, extra meticulous efforts and razor-sharp insight bundled with precise seismic damage estimation (2-×2-km grids) were conducted for the downtown area to provide guidelines for emergency response. The proposed GBR framework provides a useful tool to quickly assess the expected damages in response to a major seismic event, which can be updated easily during disaster.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".