A framework for the rapid assessment of seismic upgrade viability using performance‐based earthquake engineering
Bibliographic record
Abstract
The performance‐based earthquake engineering (PBEE) methodology allows designers to deaggregate expected seismic losses in a building to a component level. This deaggregated information provides the opportunity to tailor upgrade strategies to individual structures based on sources of losses. However, the optimization of an upgrade strategy becomes difficult because of the relationship between a structure and its nonstructural components; hence, multiple competing upgrade options must be considered. To address this obstacle, this article proposes a framework to guide the assessment of the viability of both structural and nonstructural upgrade strategies, while accounting for limited design resources likely encountered in the early stages of the design process. The framework utilizes the median shift probability (MSP) method, a modified version of the PBEE method introduced in this article, to rapidly summarize the effects of structural upgrades on nonstructural components by considering the impacts of structural modifications on the floor hazards. While accounting for this relationship, the MSP method utilizes the deaggregation of loss across different source categories to identify the benefit of combined structural and nonstructural upgrades, increasing a designer's understanding of the impact of structural upgrades on losses and allowing for the rapid determination of optimized upgrade strategies unique to the owner's conditions. A case study example of the implementation of the framework is provided, and the results obtained from the MSP method are compared with those obtained from more rigorous but resource‐intensive optimization analysis. An implementation of the MSP method in Microsoft Excel is provided with this article.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".