Assessing Seismic Vulnerability - Part 1
Bibliographic record
Abstract
In 2002, the New York State Department of Transportation (NYSDOT) began a pilot program to assess the seismic vulnerability of bridges in New York. This article, the first in a two-part series, describes how this assessment was conducted. The project involved assessing 450 bridges on and over various interstate highways that are designated as New York City emergency routes. A 1995 NYSDOT seismic vulnerability assessment manual was used, with the project also seeking to determine if modifications to the manual's approach were necessary. The seismic vulnerability assessment consisted of procedures for screening, classifying and rating a bridge. Screening a bridge assigned it to one of four susceptibility groups based on the bridge's configuration. Classifying entailed recognizing substandard or vulnerable features of the bridge and potential for liquefaction-related damage, which in conjunction with the soil type, yielded a low, medium or high vulnerability classification score. The last step was to identify a potential failure mode for the bridge and assign a rating. The procedure provides a qualitative evaluation rather than a detailed analysis. The main purpose was to group structures by the vulnerability to a seismic event in order to determine if further action is needed. Many of the assessment were straightforward; issues related to bridges that did not fit well into the assessment procedure are discussed in the second part of this article.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".