Bibliographic record
Abstract
Terrorist attacks, natural hazards, and accidents have many similarities in their effects and possible mitigations. Once a hazard occurs, it is the responsibility of the owner to provide protection, facilitate response, and plan for disaster recovery. This can be categorized as all-hazards or multi-hazard preparedness. This article discusses the role of transportation professionals in multi-hazard preparedness. Transportation facility owners can be more effective in saving human lives by planning for and facilitating rescue and response to reduce potential human casualties. Additional research, modeling, and testing of complex structures such as bridges under extreme loads is needed for engineers to fulfill their broader role to achieve resiliency for critical transportation infrastructure in a multi-hazard event. The advantages of the resilient infrastructure approach to multi-hazard events include: priority on life safety and emergency response issues through pre-event preparedness; reducing the socioeconomic impact of the loss of critical infrastructure from an extreme event; post-event, the precise location and extent of damage to a system is known and resources can be effectively and quickly focused on the location that requires repair; and overall investment of resources is the most efficient and cost effective. The challenges for the engineering community include the following: find cost-effective measures to facilitate evacuation, response, and rescue without unknown dangers to first responders; find cost-effective measures to restore service and shorten the overall recovery period through means and methods of rapid reconstruction; focus on multi-discipline improvements to life-safety systems; improve tools for prediction of damage and isolation of damage; and develop innovative technology solutions for situational awareness and the monitoring of structural behavior during extreme events. Most important to reaching the objective of multi-hazard engineering and achieving transportation infrastructure resiliency in disasters is the acceptance of the new and broader role for engineers and a new philosophy for the engineering of transportation facilities and systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".