Highway Bridge Infrastructure in the Province of British Columbia (BC), Canada
Bibliographic record
Abstract
Some recent catastrophic impacts on highway bridges around the world have raised concerns for assessing the vulnerability of existing highway bridges in Canada. Rapid aging of bridge infrastructure coupled with increased traffic volume has made it crucial to establish an advanced Bridge Management System (BMS) for highway bridges. This paper aims at developing a highway bridge inventory for the province of British Columbia (BC) which is critical for efficient assessment of the existing structural health condition of the bridges, predicting their future deterioration, and prioritizing their maintenance and retrofitting works. This inventory is an extensive assemblage of data on highway bridges in BC under the responsibility of the BC Ministry of Transportation and Infrastructure (BC MoT) that includes more than 2500 highway bridges. It includes identification of the most common bridge types along with their location, structural and geometric parameters such as construction materials, bridge length, number of spans, deck width, skew angle, bridge pier, and foundation type, structural health condition rating and construction period. This information is of paramount importance for effective infrastructure management, proper rehabilitation solutions, and efficient design of a Structural Health Monitoring (SHM) and Control System for enhancing structural resilience of highway bridges in BC. Several statistical analyses have been carried out for efficient utilization of the information available in the inventory for further research and analyses, as well as for developing a proper BMS for the province’s bridges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".