Lifetime Resilience Migration Quantification Using Nonparametric Distance Metrics and Application for River-Crossing Bridges
Bibliographic record
Abstract
Quantitative resilience assessment is an essential to the lifetime management of civil structures. With the uncertainties arising from both physical and socioeconomic dimensions, resilience needs to be measured probabilistically. Although existing resilience frameworks have addressed this need, none statistically characterize how much a system’s resilience migrates and in what direction it migrates. This paper proposes a lifetime resilience migration quantification (LRMQ) framework based on nonparametric distance metrics. In this framework, system resilience positioning is proposed by comparing the resilience of a variable system against two reference systems. Two decision-making analytics, a binary resilience classification (BRC) diagram and a lifetime resilience attenuation (LRA) model, are proposed. The proposed method is evaluated with success by performing numerical experimentation over a representative river-crossing bridge. It is observed that the bridge’s system resilience attenuates progressively in its lifetime; with the consideration of a scour countermeasure, resilience is effectively enhanced via visualizing the proposed BRC diagram and LRA model.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".