Rock-Socketed Shafts for Highway Structure Foundations
Bibliographic record
Abstract
For the past 25 years, much knowledge and experience has been acquired by the engineering and construction industries on the use of rock-socketed shafts for support of transportation structures. This synthesis collected, reviewed, and organized the most salient aspects of this knowledge and experience to present it in a form useful to foundation designers, researchers, contractors, and transportation officials. The objectives of this report were to collect and summarize information on current practices pertaining to each step of the design process, along with the limitations; identify emerging and promising technologies; determine the principal challenges in advancing the state of the practice; and provide suggestions for future developments and improvements in the use and design of rock-socketed shafts. For this TRB synthesis report a literature review was conducted on all topics related to drilled shaft in rock or intermediate geomaterials. A questionnaire was developed and distributed to the principal geotechnical and structural engineers of U.S. state and Canadian provincial transportation agencies. Questions were grouped into the following categories: use of rock-socketed shafts by the agency, evaluation of rock and intermediate geomaterials, design methods for axial loading, design methods for lateral loading, structural design, construction, and field load and integrity testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".