Bibliographic record
Abstract
Bridges are an essential component of transportation systems for traffic passage across natural or man-made obstacles. Modern urban trends and growing travel patterns have resulted in a rise in the number of bridges on motorways to avoid road conflict. A major fire might cause irreversible structural deterioration or perhaps the bridge's failure. This research studies the critical factors of bridge fire incidents using a comprehensive database containing 171 bridges. Using an Artificial Neural Network (ANN) model, the vulnerability of bridges in fire is estimated, and the extent of damage is determined based on several key factors including bridge proximity to urban, suburban, or rural areas, structural system, construction materials, annual average daily traffic (AADT), ignition source, combustible types, position of the fires, and the fire-caused damage level. The influence of each parameter on the bridge fires damage levels is investigated. Suggesting measures to reduce the risk of damage, damage levels of fire incidents in different bridges for different scenarios is predicted and a comprehensive and accurate model for definition of vulnerability of bridges under fires is proposed. The outcome of this research will help predict the risk of fire in bridges with various characteristics and the level of damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".