Estimating economic losses of midrise reinforced concrete shear wall buildings in sedimentary basins by combining empirical and simulated seismic hazard characterizations
Bibliographic record
Abstract
Summary Studies of recorded ground motions and simulations have shown that deep sedimentary basins can greatly increase the intensity of earthquake ground motions within a period range of approximately 1–4 s, but the economic impacts of basin effects are uncertain. This paper estimates key economic indicators of seismic performance, expressed in terms of earthquake‐induced repair costs, using empirical and simulated seismic hazard characterizations that account for the effects of basins. The methodology used is general, but the estimates are made for a series of eight‐ to 24‐story residential reinforced concrete shear wall archetype buildings in Seattle, WA, whose design neglects basin effects. All buildings are designed to comply with code‐minimum requirements (i.e., reference archetypes), as well as a series of design enhancements, which include (a) increasing design forces, (b) decreasing drift limits, and (c) a combination of these strategies. As an additional reference point, a performance‐based design is also assessed. The performance of the archetype buildings is evaluated for the seismic hazard level in Seattle according to the 2018 National Seismic Hazard Model (2018 NSHM), which explicitly considers basin effects. Inclusion of basin effects results in an average threefold increase in annualized losses for all archetypes. Incorporating physics‐based ground motion simulations to represent the large‐magnitude Cascadia subduction interface earthquake contribution to the hazard results in a further increase of 22% relative to the 2018 NSHM. The most effective of the design strategies considered combines a 25% increase in strength with a reduction in drift limits to 1.5%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".