Improving the Properties of Soft Soils using Nano-silica, Slag, and Cement
Bibliographic record
Abstract
Effective utilization of weak soils such as soft clay by imparting additional strength using various stabilization techniques are adopted to enhance the soil behaviour (i.e., bearing capacity) for the construction of roads and/or platforms. Applications of nanomaterials in the field of geotechnical engineering have great potential, for example by promoting the construction of a stronger and stiffer soil skeleton, especially when blended with cementitious materials. Therefore, this paper focuses on studying the effect of nanomodified cementitious binders on the properties of weak soils, which are the most common types of soil in Winnipeg, Manitoba, Canada. The soil selected was soft clay. It was mixed with GU (general use) cement, or slag, or both with different proportions of nano-silica sol (0 to 2.4% of the dried soil weight). The mechanical properties such as the compressive strength at different curing ages and California bearing ratio (CBR) were investigated. Generally, the addition of nano-silica to cement enhanced the properties of the soil in terms of maximum dry density, compressive strength, and CBR. In particular, the bearing ratio for the soil treated with the ternary binder (cement, slag, and nano-silica) was improved. Thus, nano-modified blended cement presents a sustainable and effective stabilizing additive to treat weak soils for the construction of roads with an anticipated measurable impact on reducing the life-cycle cost of repairs due to its projected stability and durability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".