Pavement Evaluation to Detect Potential Voids Under the Surface After the Historic Flood of June 2013 in the City of Calgary
Bibliographic record
Abstract
On June 20, 2013, The City of Calgary (The City) witnessed one of the worst floods in its known history. The flood was so devastating that about 100,000 people were evacuated from their residences. Public and private infrastructure suffered hundreds and hundreds of millions of dollars in damages. A state of local emergency (SOLE) was declared for 15 days. Many roadways were washed out or had developed multiple sinkholes. The affected pavement network indicated about 250 sinkholes during only the time of SOLE. Some of the roadways were showing multiple sinkholes and it was believed that the flood water was flowing in channels under the pavement surface. This channelling may have created voids that collapsed with insufficient support for the material above. To demonstrate due diligence, The City decided to investigate the roads indicating multiple sinkholes for potential voids or other conditions that may not have surfaced yet. A pavement evaluation was conducted on affected portions of five roadways (MacLeod Trail, 1st Street SE,11 Avenue SE, 25 Avenue SW, and 38 Avenue SW) for the detection of potential voids under the pavements. A forensic non-destructive testing program utilizing Road Radar and Falling Weight Deflectometer (FWD) testing was developed. Two survey passes per lane, one in each wheelpath, was carried out. Anomalies underneath the pavement structure were identified with the Road Radar system. The locations identified with anomalies were tested with FWD for further analysis. The spacing and location of the FWD tests were determined based on the severity, size, distribution and location of anomalies. Benchmarking criteria for the evaluation of the FWD deflections were established. All data was compiled into GIS that was used to reference all components of the data and aid analysis. This paper discusses the process of survey, Road Radar, FWD testing and analyses, a brief literature search, and developing deflection criteria and benchmarking. Conclusions and recommendations are presented at the end.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".