Innovative Overload Permitting in Manitoba - Allowing a 363 250 Kg (GVM) "Superload"
Bibliographic record
Abstract
Manitoba Infrastructure and Transportation (MIT) has over 1000 Bridge structures on their highway network that are nearing the end of their service life. Faced with an increasing number of overload permit applications and aging structures, a refined method of load rating is needed to permit overloads efficiently while avoiding damage to bridge structures. MIT uses AASHTO's Non?Standard Gage (NSG) Distribution Factor Method to predict the performance of superstructure elements for bridges. These particular overloads weighing 80 percent more than the design loads have been reviewed and approved using the NSG method. MIT is the only Agency in Canada that uses the AASHTO Load and Resistance Factor Design (LRFD) Bridge Design Specification and the corresponding Load and Resistance Factor Rating (LRFR) specification for bridge load rating. AASHTO's Bridgeware rating program (Virtis) employs the NSG method for load rating and using this method, MIT was able to issue permits for safely moving three transformers each weighing 357 000 kg (Gross Vehicle Mass, GVM). Field monitoring and measurements were undertaken in order to compare the structure's actual performance against theoretical values. This Paper will discuss the details of the load rating analysis using the Non?Standard Gage Distribution Factor Method, the instrumentation used for monitoring, and the results of the comparison between actual and theoretical values. By utilizing Virtis, MIT rated the bridges along a predetermined route and obtained Operating Rating Factors which were greater than one; hence the transformers were successfully moved and upon inspection, the structures behaved as predicted. For the covering abstract of this conference see ITRD record number 201310RT334E.
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 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".