Case Study of an Innovative Forensic Investigation of a Dramatic Pavement Failure, 14 Street NW, Calgary, Alberta
Bibliographic record
Abstract
On the evening of June 5, 2007, the City of Calgary experienced an extreme weather event; over 70 mm of rainfall, much of which fell within one hour. This resulted in significant consequences in terms of surface runoff, storm sewer capacity and other related pressures on surface and subsurface infrastructure. Likely the most dramatic of the damage was the resulting distress to an approximate one kilometer section of 14 Street NW, a primary north-south commuter corridor accessing the City downtown core. Surface damage included severe upheavals in both the roadway and adjacent sidewalk, the extent and magnitude of which required closing of the facility to both vehicles and pedestrians. The City of Calgary, Roads Division, commissioned EBA Engineering Consultants Ltd. to undertake an integrated pavement and subsurface assessment program. This paper describes the innovative pavement assessment methodology and data presentation features, along with the methods used to identify the cause, nature and extent of pavement and subsurface distress. The design concepts employed for the necessary restoration are provided as well as several innovative project delivery concepts and construction details. These aspects were focused on addressing the impacted infrastructure, mitigating the reoccurrence of this phenomena, and fast-tracking the project to minimize traffic disruption on this primary route.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".