Thermal effects on tensile strength of a compacted soil
Bibliographic record
Abstract
Soil tensile strength holds paramount significance in many geotechnical applications, frequently encountering non-isothermal conditions. This study aims to investigate thermal effects on tensile strength of a compacted lean clay, considering various dry densities and microstructures induced by varying compaction water contents during desiccation process. Direct tensile tests are conducted to assess the tensile strength of each soil specimen. Experimental findings demonstrate that both dry density and compaction water content significantly influence tensile strength. Higher soil density leads to reduced void spaces, increasing contact points and friction, ultimately enhancing tensile strength. Moreover, higher compaction water content shifts the soil structure from aggregated to dispersed, reducing pore size and increasing inter-particle contact forces, resulting in greater tensile strength. Regarding thermal effects, elevated temperatures reduce soil tensile strength due to increased double layer repulsion forces and decreased suction-induced inter-particle normal forces. In terms of sensitivity to temperature changes, higher dry densities render the soil specimen less susceptible to temperature fluctuations. The soil specimens compacted with a dispersed microstructure on the wet side exhibit the highest sensitivity to temperature changes, followed by specimens compacted at the optimum water content. In contrast, those compacted on the dry side with an aggregated microstructure display the lowest sensitivity.
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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".