The 1999 R.M. Hardy Lecture: The implementation of unsaturated soil mechanics into geotechnical engineering
Bibliographic record
Abstract
The implementation of unsaturated soil mechanics into geotechnical engineering practice requires that there be a paradigm shift from classical soil mechanics methodology. The primary drawback to implementation has been the excessive costs required to experimentally measure unsaturated soil properties. The use of the soil-water characteristic curve has been shown to be the key to the implementation of unsaturated soil mechanics. Numerous techniques have been proposed and studied for the assessment of the soil-water characteristic curves. These techniques range from direct laboratory measurement to indirect estimation from grain-size curves and knowledge-based database systems. The soil-water characteristic curve can then be used for the estimation of unsaturated soil property functions. Theoretically based techniques have been proposed for the estimation of soil property functions such as (i) coefficient of permeability, (ii) water storage modulus, and (iii) shear strength. Gradually these estimations are producing acceptable procedures for geotechnical engineering practices for unsaturated soils. The moisture flux ground surface boundary condition is likewise becoming a part of the solution of most problems involving unsaturated soils. The implementation process for unsaturated soils will still require years of collaboration between researchers and practicing geotechnical engineers.Key words: unsaturated soil mechanics, soil suction, unsaturated soil property functions, negative pore-water pressure, matric suction, soil-water characteristic curve.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".