Investigation of the compressive strength of engineered water repellency in natural soils under varying environmental conditions
Bibliographic record
Abstract
The persistent degradation of soil stiffness and strength due to moisture fluctuations can be mitigated through Engineered Water Repellency (EWR). This technique alters the wettability of soils using organosilane (OS), modifying the soil surface without forming cementitious bonds. This study evaluates the performance of EWR-treated soils under varying environmental conditions, including air drying, wet-dry cycles, and prolonged immersion, by assessing the unconfined compressive strength (UCS) of two EWR-treated soils. The soils were treated with different OS concentrations and subjected to up to 120 days of immersion and 21 wet-dry cycles. The UCS of treated samples was measured as the hydrophobicity of the EWR soils developed during drying. X-ray CT scans were used to analyze porosity changes and internal pore structures post-exposure to the varying environmental conditions. The results showed that OS treatment reduced the optimum moisture content while having minimal impact on maximum dry unit weight. However, mechanical strength decreased as OS concentration increased, attributed to the organic moiety of the OS molecule siloxane bond formation, which reduced compressive strength. However, EWR-treated soils maintained their structural integrity during extended water immersion, with higher OS concentrations offering better resistance to wet-dry cycles. Over 120 days of soaking, EWR-treated soils experienced strength reductions due to increased porosity and excess unbound OS. These findings contribute to a deeper understanding of hydrophobic soils, providing valuable insights into the mechanical strength of EWR soils and enhancing the feasibility of applying this technology for subgrade modification.
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 | 0.000 | 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".