Seismic risk assessment and mitigation analysis of large public school building portfolios in Metro Manila
Bibliographic record
Abstract
This article presents an integrated framework for portfolio seismic risk assessment. The framework includes a systematic approach to the collection and categorization of exposed assets, and a robust method that combines vulnerabilities of different levels of resolution, including detailed vulnerabilities based on Federal Emergency Management Agency (FEMA) P‐58 and REDi analyses, to produce efficient portfolio‐level assessment. The vulnerability information encodes risk information that directly addresses the decision‐support needs of stakeholders, including the benefits of seismic retrofit. The proposed framework is applied to a spatially distributed infrastructure portfolio composed of over 1000 public school buildings across Makati and Quezon City, in the Metro Manila region in Philippines, to demonstrate the value of portfolio‐level seismic risk mitigation strategies. This study illustrates that the proposed regional seismic risk assessment approach can provide more reliable regional risk assessments in a computationally efficient manner and is able to quantify different risk contributors and identify cost‐drivers that can be targeted for performance‐based risk management of large portfolios.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".