Field Sampling and In Situ Testing of Soil-Cement Mixtures Used in Soil Mixing and Slurry Trenching
Bibliographic record
Abstract
Soil mixing and soil-bentonite-cement slurry trenching methods produce a soil-cement product that can be classified as an intermediate geo-material (IGM). Field sampling and testing for quality control are critical in evaluating the engineering properties of these materials. An effective field sampling and testing program is one that provides an accurate representation of the resulting IGM. While there are several standards for field sampling and testing geo-structural materials, few were developed specifically for soil-cement mixtures created by soil mixing or slurry trenching methods. Available field sampling and testing methods and standards originally developed for other materials need to be carefully applied to soil mixing and slurry trenching projects to ensure that the results are both meaningful and representative of the tested materials. Specifications for soil mixing and slurry trenching projects sometimes contain field testing standards or sampling/testing requirements that are not well suited for the resulting geo-materials. This paper on field sampling and testing complements a previous paper the authors published in the 2023 DFI Sixth International Conference on Grouting & Deep Mixing proceedings, which focused on laboratory testing methods. The objective of this paper is to discuss field sampling and testing methods, such as coring, in situ permeability testing, test pits, sonic drilling, and thin-walled tube sampling, and the applicability of those methods for soil-cement mixtures created in the stated applications. The paper provides recommendations for modifications to testing methods, where appropriate, for use in soil mixing and slurry trenching applications.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".