Evaluating the Influential Factors on Cr(VI) Leaching from Compacted Cement-Stabilized Soil through Tank Leaching Tests
Bibliographic record
Abstract
In Japan, one commonly uses soil improvement techniques, involving the application of cement-based stabilizer, which is readily available locally and can achieve sufficient compressive strength within a short period.However, previous research has indicated potential environmental concerns associated with the use of cement stabilized soil, since cement materials contain certain amounts of hexavalent chromium (Cr(VI)).In addition, in 2022, the environmental criterion for Cr(VI) concentration in groundwater was lowered to 0.02 mg/L in Japan.Considering these conditions, a comprehensive understanding of the leaching behavior of Cr(VI) released from the cement-stabilized soil is essential for evaluating their environmental consequences.This research investigates Cr(VI) leaching from the cement-stabilized soil by conducting the tank leaching tests (TLTs), focusing on influential factors such as mixing methods (dry (DM) and wet (WM) methods), curing periods, specific surface areas of the specimens, and liquid-to-solid ratios.The results indicate that the Cr(VI) leaching concentrations of the WM specimens are much lower than those of the DM specimens of equivalent unconfined compression strength, In WM, Cr(VI) is undetectable due to specimen homogeneity and enhanced hydration of the cement.Currently, the leaching concentration after immersing specimens for 28 days is measured according to the Japanese regulation.However, this may lead to underestimation of the leaching rate of Cr(VI) concentration, as the highest leaching concentrations are observed before 28 days.Moreover, the effect of curing periods suggests that the specimens cured for 7 days exhibit higher Cr(VI) leaching concentrations than those cured for 28 days, attributed to the enhanced precipitation of Cr(VI) by hydration products such as Ca(OH)2.A quicker leaching rate is achieved by a higher specific surface area.It is apparent that considering these effects is important to determine the appropriate regulations for the TLT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".