Evolution of truck weight limits and bridge live load models: evidence of impacts from Manitoba, Canada
Bibliographic record
Abstract
This research presents evidence of the impacts of truck size and weight regulation changes on bridge live load models, truck operating gross vehicle weights and bridge structure reliability, specifically focusing on the context of Manitoba, Canada. This work addresses previous research gaps and contributes knowledge to this topic by: (1) examining historical relationships between truck size and weight regulations and bridge design codes; (2) conducting a retrospective, longitudinal study using a unique time- series of truck weight data to provide insights about the magnitude and timing of the impacts of truck weight regulatory changes on truck operating gross vehicle weights; and (3) evaluating the reliability of typical bridge types designed according to modified live load models and subject to differing truck live loads. Key findings from this work include several insights and observations. First, while at times bridge codes are released in conjunction with expected regulation changes, there is often a delay in the issuance of revised design codes, thus highlighting the importance of assessing current changes to the truck fleet for future bridge code live load models. Second, truck carriers take advantage of increased truck weight limits; however, the truck configuration and type of commodity influences the sensitivity to the weight limit changes. Similarly, the timing of the response is dependent on the ease of implementation and immediate increases are observed for weigh-out commodities when fleet modifications are not required. Third, three bridge types designed to the previously unanalyzed live load model specified in Manitoba are found to maintain a level of safety for the girders that exceeds target code requirements with reliability indices ranging from 4.65 to 5.04, depending on the considered live load statistics. This indicates a potential capacity for the considered structure types to safely withstand increased traffic load effects. By studying specific topics influenced by truck size and weight regulation changes and providing evidence of the impacts on truck operating weights and bridge infrastructure, this thesis contributes new insights to address gaps in previous research. It highlights the importance of monitoring changing truck weights and configurations to maintain the level of safety of bridge infrastructure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".