Solidification of Subgrade Materials Using Magnesium Alkalinization: A Sustainable Additive for Construction
Bibliographic record
Abstract
The stabilization of problematic soils with chemical additives has become a popular practice globally. However, the mechanical and microstructural characterization of subgrade materials stabilized by alkalinization of raw silty sand, a common soil in British Columbia, Canada, has not yet been studied. This study introduces the novel concept of using an alkaline activator, along with magnesium chloride (MgCl2), to activate the silica and alumina components of silty sand. Compaction and unconfined compressive strength (UCS) tests were used to assess the mechanical properties of the stabilized soil. The mechanisms that have contributed to the stabilization process are discussed based on the results of microstructural analysis using field-emission scanning electron microscopy (FESEM), energy-dispersive spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR) analysis. It was found that the chemical additive improved the compressive strength of the soil significantly. The UCS results revealed that a sample mixture containing an alkaline activator (sodium silicate/sodium hydroxide) ratio of 0.5, an alkaline activator to MgCl2 (L/S) ratio of 0.7, and 3% MgCl2 by dry weight of the soil was the optimum mix to improve the strength of the silty sand when cured for 28 days. The FTIR result confirmed the formation of the magnesium hydration products. Additionally, the SEM images and EDS data revealed that the stabilization process produced a cementitious gel, consisting of magnesium silicate hydrate (M-S-H) and magnesium aluminate hydrate (M-A-H) compounds, that bonded soil particles together.
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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 | 0.000 | 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) | 0.001 | 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".