A Review of Physicochemical Stabilization for Improved Engineering Properties of Clays
Bibliographic record
Abstract
Severe climatic and environmental conditions warrant the use of stabilization agents in aid of compaction for sustainable improvement in engineering properties of clays. Physicochemical agents are a viable option because they are cost effective, environmentally friendly, and offer improved long-term performance of treated soils. This research developed a fundamental understanding of the clay–water–electrolyte admixtures relations. Based on a comprehensive literature review, the effect of nanomaterials, biopolymers, and geopolymers on the behavior of compacted clays was investigated. It was found that all of these admixtures facilitate the development of an aggregated soil microstructure through unique mechanisms. Biopolymers have the highest water adsorption capacity followed by geopolymers and then by nanomaterials. The effect of admixtures on optimum compaction properties follows a decreasing trend similar to untreated clays (S = 80% ± 20%). The variation of hydraulic conductivity, compression index, and compressive strength are largely within the family of curves identified by typical relationships for compacted clays. These preliminary findings indicate that not all engineering properties are improved to the same level by the different types of physicochemical admixtures. The specific nature of geotechnical engineering (soil type and site conditions) as well as the wide range of admixture types and potential biodegradation of some of the reagents are the major shortcoming of using this class of materials.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".