Bibliographic record
Abstract
The deep mixing method of ground improvement blends additives to soil in-situ to increase strength, increase stiffness, and/or decrease hydraulic conductivity. The deep mixing method can be used to support embankments, structures, and excavations; mitigate liquefaction; and create seepage barriers. Contractors continually develop new equipment and procedures to improve the quality and decrease the cost of deep mixing. Equipment types include vertical-axis mixing rigs with one to six shafts, horizontal axis mixing machines with two rotating cutting wheels or one rotating drum, and chain-saw type mixing machines with a vertical post. Cutting and mixing teeth and blades vary in number, type, and orientation. The binder can be injected as dry powder or wet slurry, and air-slurry emulsions have been used. Slurry can be injected on the down-stroke or the up-stroke under low, moderate, or high pressure, and the injection nozzles can be located at the bottom of mixing shafts, along mixing blades, or from the shaft just above the mixing blades. Not all equipment and procedures are suitable for all soil conditions or project types, but more than one approach is feasible in most situations. Collaboration among owner, engineer, and contractor enable the benefits of innovations, and different contracting mechanisms enable different degrees of collaboration. Even in a design-bid-build contract, the engineer can develop the design and prepare the plans and specifications to allow for contractor innovation while protecting the owner’s interests. Strategies include: expressing the geometric requirements in normalized terms, with appropriate limits; using statistically based acceptance criteria; making full use of construction quality control records; and involving the design engineer during construction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".