Development & Implementation of a Structures Management System for a P3 Contract
Bibliographic record
Abstract
On February 4, 2005, Brun-Way Highways Operations Inc. (BHOI) entered into a 28 year and one month contract with the Provincial Government, to operate, maintain and rehabilitate (OMR) approximately 275 kilometres of four-lane highway in New Brunswick. The contract consists of approximately 261 kilometers along Route 2 between Longs Creek, just north of Fredericton to the Quebec border, and the remaining 14 kilometers along Route 95 at the intersection of Route 2 in Woodstock to the state of Maine. A significant requirement of the contract was the implementation of a Structures Management System (SMS) for bridges, overpasses, underpasses, drainage structures with a span length greater than three meters and overhead sign trusses within the highway corridors. The objectives of the SMS were to achieve asset preservation to ensure all structures are well maintained throughout the contract duration and meet the contractual requirements for the minimum remaining life at the contract termination date. SNC Lavalin ProFac (SLP), one of the BHOI partners, has significant experience with the management and maintenance of buildings and has developed asset management systems specifically for these facilities. BHOI worked with SLP to modify their existing system for buildings to suit the needs of a highway facility. This included providing a central applications database capable of inputting, storing, assessing, forecasting and reporting on approximately 190 separate structures. The development of this database included gathering tombstone data, conducting structure inspections following the American Association of State Highway and Transportation Officials (AASHTO) Bridge Inspection Standards and developing a condition rating for each component to establish an overall Health Index for each structure. This paper describes BHOI’s SMS, the challenges encountered during the development, the associated advantages and disadvantages of adopting such a system, recommendations for future considerations compatible with this system and conclusions assessing the effectiveness of this system.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".