Portwide Seismic Risk Assessment: (1) Engineering Analyses
Bibliographic record
Abstract
The Port of Portland, Oregon recently (2015) completed a comprehensive, port-wide seismic risk assessment as part of a long-term plan to improve the Port’s seismic resiliency. The Port targeted 20 of their most important marine and aviation assets for this risk analysis and retained a team to develop and apply a seismic performance evaluation of each asset. A multi-hazard level approach was implemented that addressed the range of return periods (i.e. ground motions) commonly applied for both marine and building structures. The seismic performance of each asset was evaluated using inertial loading based on estimated site-specific ground motions and kinematic loading based on estimated seismically-induced soil displacements. This paper focuses on the geotechnical and structural approaches utilized for the marine facilities, and the integrated approach to evaluating dynamic soil-structure interaction for the waterfront structures. Key discussion points include the synthesis and application of archival data, the need for site-specific ground motions due to limitations in code-based soil factors, appropriate level of analyses for seismic risk assessment, and considerations associated with structural and geotechnical mitigation strategies suggested for the subsequent benefit/cost analyses. These analyses provided requisite input for the subsequent port and regional seismic risk analyses addressed in the companion paper by Graf and others (2016).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".