Some experiences from the introduction of wet deep mixing in Scandinavia
Bibliographic record
Abstract
The dry deep mixing (DDM) method has been used extensively – and exclusively – in the Nordic countries since its development in the 1970s. Despite being a versatile method, it is mainly limited to sensitive clays with low shear strengths and the method has limitations due to considerable verticality deviations. However, increasing ground improvement needs for stiffer clays, improved homogeneity, and better verticality at greater depths has led to the introduction of the wet deep mixing method (WDM) in both Sweden and Norway. This paper summarizes some experiences from three projects where WDM recently have been employed both onshore and offshore to increase stability and reduce settlements. Two Swedish projects, one large scale commercial and one trial test, were performed in clays with low shear strengths and high water contents for stability purposes offshore for land reclamation. The Norwegian project involved improvement of a low water content clay for settlement reduction of a building foundation. Results from strength verification using wet grab and core sampling are presented and are compared to experiences from DDM and preceding laboratory tests. The paper furthermore discusses some practical issues around execution and strength verification in the three projects. Overall, it is concluded that WDM in many cases is suitable also for Scandinavian clays and can therefore replace or supplement DDM. However, the lack of experience still calls for certain conservatism. Trial columns are recommended until there is more field experience from WDM in Scandinavian clays.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".