Utilization of Ground Improvement for a Variety of Cost Effective Remediation and New Construction Topics for the Rail Industry
Bibliographic record
Abstract
The authors of the paper represent two firms that have completed hundreds of challenging subgrade and foundation projects for the rail industry. The intent of this paper is to educate the railroad business in general about alternative approaches to common geotechnical problems facing the railroad industry. Projects have been completed across the country in nearly all geological conditions, on all of the Class I carriers, Shortlines and Mass Transit systems. Successful remediation projects associated with challenging subsurface conditions across the United States, Canada and Mexico are covered. Case histories include jet grouting for low headroom earth retention and tunnel support, stone columns for embankment support, micropiles for low headroom bridge replacement, micropiles and soil nails for earth retention, compaction, and urethane grouting for settlement of existing structures. Projects discussed include background information such as project layout, drawings and test results. Each project is completed and has a positive track record, indicating success. Projects have been specially selected to demonstrate the ability of specialty foundation solutions applicable throughout North America. Each topic provides technically sound approaches to age-old Rail road subsurface challenges. Many of these topics are not addressed in the AREMA manual; however, one of the authors, is currently addressing these topics through a proposed section of AREMA chapter 8.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".