Review of Resilience Evaluation Methods in Operational Highway Tunnel
Bibliographic record
Abstract
The research on tunnel resilience has garnered increasing attention in recent years. Owing to prolonged exposure to natural or anthropogenic factors, the resilience level of many highway tunnels is continuously declining, rendering them susceptible to sudden accidents and challenging to restore postincident. Currently, although several scholars have employed diverse evaluation methods in their research on tunnel resilience, there is a lack of summarization and integration of these methods. In addition, there is also a dearth of a unified evaluation index system and framework for different types of natural or man‐made disasters, which are crucial for advancing the development of tunnel resilience evaluation. This study commences with an introduction to the origin of resilience and the definition of tunnel resilience, and comprehensively summarizes commonly employed evaluation methods. Subsequently, this study centers on the resilience evaluation methods in tunnel engineering and analyzes their strengths and weaknesses. Besides, the distribution of resilience metrics in current researches is analyzed and the detailed explanations for the diverse choices are provided. According to the results and deficiencies of existing research, combined with the author’s perspectives, the index systems, evaluation frameworks, and resilience improvement strategies are proposed, which can be applied to the resilience evaluation of various operational highway tunnels under diverse disaster scenarios. Furthermore, this study also presents a case study on the evaluation of tunnel fire resilience to validate the applicability of the research findings. These findings aim to provide a guide for the operation and maintenance management of the operating tunnels and improve the scientific decision‐making level of tunnel maintenance.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".