Bibliographic record
Abstract
With the aging of its infrastructure, Canada is facing a critical problem to deal with the complex and fragmental issues existing in current infrastructure management. Because Bridge Management Systems (BMSs) are not used in a systematic way in some provinces in Canada, this research aims at reviewing the current state of BMSs in Canada and suggesting an initiative to build a Canadian National Bridge Inventory. For example, The Bridge Expert Analysis and Decision Support (BEADS) system currently used in Alberta is different from the BMSs of other provinces in its system structure and scope. BEADS is an important part of a comprehensive system--Transportation Infrastructure Management System (TIMS). The Ontario BMS integrates the deterioration model, cost model, and business rules for treatment selection and costing, and an analytical framework for calculating and representing information relevant to the decision at hand. The Quebec BMS has three main models (Deterioration Model, Treatment Model, and Cost Model) that are used to create work alternatives at the element, project, and program levels. After comparing the above BMSs, the research discusses a new research project at Concordia University to build a Canadian National Bridge Inventory (CNBI) similar to the NBI used in the U.S.A. As a case study about the information that can be used in the CNBI, the database of Quebec BMS has been analyzed in detail and a graphical user interface (GUI) of Quebec bridge database has been developed. The inventory part of this database is proposed as an example that can be modified in the future to be used as the base for the Canadian BNI. Next, the database is used to perform some calculations related to replacement cost of bridges. The effect of three factors on the replacement cost of Quebec bridges are studied including the age of bridges, their structural type, and location. However, the current data are not enough to analyze the replacement cost. Therefore, personal records for each bridge to monitor bridge status from the design stage to the end of design service life should be included in bridge database. Finally, a method for assessing Quebec bridges is explained. In addition, a new depreciation method is introduced based on the traditional straight line method and considering the effects of traffic volume. As the example demonstrated, the annual depreciation value and the depreciation period are related more closely to actual use. The proposed depreciation method would result in a more accurate assessment of bridge assets as capital stock.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".