Design, Construction, and Performance of a Highway Embankment Failure Repaired with Tire-Derived Aggregate
Bibliographic record
Abstract
In July 2006, a large embankment failure occurred during construction of a four-lane divided highway leading to the Canada–U.S. border crossing in St. Stephen, New Brunswick, Canada. The highway embankment was approximately 12.3 m in height, just short of the design height of 14 m, when it failed. The cause of the failure was attributed to the rapid rate of construction and the intensity of loading on low-strength foundation soils, consisting of up to 15 m of soft marine clay. The reconstruction effort used 1.4 million scrap tires to create the tire-derived aggregate (TDA) as lightweight fill. TDA was placed within the new embankment, constructed over the site of the original failure. An important element to the design was the installation of geotechnical instrumentation, which allowed an observational approach to be taken during the construction process. This approach resulted in modifications to the original design throughout the process of reconstructing the TDA embankment. This paper presents the results of the TDA embankment and foundation performance over 25 months, with an emphasis on the design and behavior of the TDA during construction. The predicted values versus the values measured in the field for geotechnical parameters and the performance characteristics of TDA are presented, including unit weight, temperature, immediate compression, and time-dependent compression. The values for these measured parameters are compared with those from similar case histories and published values available from the literature. The TDA embankment reconstruction project began in the summer of 2007 and was successfully completed in the late fall of 2008.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".