A simple technique for estimating the one-dimensional heave of natural expansive soils
Bibliographic record
Abstract
Expansive soils are considered to be a nightmare to the geotechnical engineers. As the losses to the infrastructure constructed on expansive soils is estimated to be in billions of dollars world wide annually, several researchers and practitioners from various regions of the world have made significant contributions to better our present understanding of these problematic soils. One of the topics that attracted interest is related to the 1-D heave prediction or estimation methods for expansive soils. Early research studies on this topic were focused on developing empirical relationships which are not universally valid. Current techniques use soil suction methods and oedometer test methods to predict or estimate the 1-D heave; however, the various soil parameters required in these techniques can only be obtained from time consuming laboratory or in-situ tests that are expensive and difficult to be performed by conventional geotechnical engineers. In the present study, a simple technique is proposed to estimate the 1-D heave in expansive soils which requires only the information of plasticity index, Ip and variation in water content with respect to depth in the active zone. This technique is developed using the results of 5 case studies published in the literature. In addition to these case studies, 8 other case studies results of 1-D heave in expansive soils are summarized. The data of 13 of the case studies is collected from various regions of the world which include Australia, Canada, Chile, Saudi Arabia, Sudan, United States and Yugoslavia. Comparisons are provided between the measured and estimated 1-D heave for all the case studies have been using the proposed simple technique. There is a reasonably good comparison between the measured heave and the estimated heave for 8 of the 13 case studies results (i.e., less than 30%). The estimated heave is more than the measured heave for all the case studies; which is conservative from an engineering practice perspective. The reasons associated with the discrepancies between the measured and predicted 1-D heave values for the remainder of the five case studies are also discussed. The proposed simple technique is encouraging for the practicing geotechnical engineers in the estimation of 1-D heave in expansive 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".