Selection of characteristic values of spatially variable cement-treated clays for deep excavations
Bibliographic record
Abstract
Cement-treated soils (CTSs) are commonly used to improve in situ soft clays. However, properties of CTS are often highly variable, posing significant challenges in the analysis and design of geosystems. This paper proposed a framework to determine the characteristic strength and stiffness parameter values for the design of CTS slabs in deep excavations under two typical modes of failure, while considering spatial variabilities. First, the proposed framework provided a series of charts for engineers to select the characteristic strength and stiffness parameter values of CTS. A comprehensive set of in situ test results from a Singapore site is then used to create a database of the statistical and spatial variabilities of CTS. Finally, a deep excavation case study in Singapore is used to illustrate the proposed charts. The results show that the characteristic strength value of CTS can range from 25% to 70% of the mean value depending on the magnitude of the statistical and spatial variabilities and the geometry of the CTS slab. Through a comparison with national design codes, the proposed framework is demonstrated to provide to a more rational selection of characteristic parameter values, leading to a more economical design of CTS slabs in deep excavations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".