Probable Hazards and Threats on Urban Tunnels during Operation
Bibliographic record
Abstract
Nowadays in megacities, subways and urban tunnels are recognized as one of the most important key components of urban transportation network and its infrastructure. Construction of these infrastructures in developed countries and gaining efficient results as an outcome of their application in reducing urban transportation issues necessitates the requisiteness of attention on their widespread utilization in developing countries. However, the design and construction of such massive projects in the country which, remarkable costs are spent for, might always be involved with the occurrence of any hazard or threat. These issues are ever considered as one of the main challenges of tunneling during all three phases of design, construction, and operation, and in order to lead to the advancement and sustainable development of these underground structures in terms of construction and maintenance, the investigation of safety and risk assessment against these hazards and threats must be seriously taken into account. In this regard, the first step is the identification of hazards and threats and the investigation of the consequence of their occurrence on urban tunnels. In the operation phase, the study of various hazards and threats on urban tunnels shows that hazards including unintentional fire, collision of vehicles, entrance of surface water and flood into the tunnel, occurrence of earthquake, and negligence in maintenance and repair of tunnel, as well as threats such as explosion, emission of chemical, biological and radioactive agents, cyber-attacks, intentional fire, and subversive operations must be considered in the risk management and safety provision of urban tunnels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".