Model for predicting tensile strength of unsaturated cohesionless soils
Bibliographic record
Abstract
The influence of tensile strength on the behaviour of cohesionless soils is typically ignored in geotechnical engineering practice. However, the tensile cracking and subsequent failure characteristics of earth structures, such as dams, slopes and embankments, are significantly influenced by the tensile strength. For this reason, a semi-empirical model is proposed for predicting the variation of the tensile strength of unsaturated cohesionless soils with the degree of saturation, using the soil-water characteristic curve (SWCC) as a tool. The proposed model is capable of predicting the tensile strength arising from matric suction and surface tension, which are related to saturated pores and to the air–water interface associated with water bridges around interparticle contacts in unsaturated pores, respectively. Information about (i) the matric suction (ua – uw), the capillary degree of saturation (Sc), and the residual degree of saturation (Sr) derived from the SWCC; (ii) the mean particle size (d50) and the coefficient of uniformity (Cu) from the grain-size distribution curve; (iii) the void ratio (e); and (iv) the friction angle ([Formula: see text]) at low normal stress level is required to employ this model. The proposed model is validated by comparing the prediction results with measured tensile strength of 10 different unsaturated cohesionless soils (including five sandy soils and five silty soils). The proposed model is promising for use in engineering practice applications as it only requires conventional soil properties, alleviating the need for cumbersome experimental studies for the determination of tensile strength of unsaturated cohesionless soils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".