Testing and prediction of mechanical characteristics of sensitive marine clays stabilized by deep mixing method
Bibliographic record
Abstract
Sensitive marine clays (SMCs) often pose considerable problems in the construction of embankments for transportation structures. In this study, extensive mechanical, microstructural, and monitoring experiments were carried out to evaluate the evolution of mechanical properties of SMCs stabilized via Deep Mixing Method. The results indicate that unconfined compressive strength and secant modulus increase with curing time. A significant improvement in mechanical properties is observed at early ages. Higher binder contents produce higher mechanical properties after same curing period. However, excess binder content does not provide significant improvement effects. The addition of ground granulated blast furnace slag (GGBFS) results in higher mechanical properties after long-term curing, and the enhancing degree is more evident with a higher proportion of GGBFS. But the situations are reversed at young age due to the “retarding effect” of GGBFS. These observations are also supported by results of physical properties, mercury instruction porosimetry, suction monitoring, and X-ray diffraction analyses. In addition, predictive models are established based on elastic-plastic theory and binder hydration model. The developed models are implemented in COMSOL Multiphysics and validated against experimental results. A good agreement is observed between experimental and predicted results which confirms the ability of developed models to predict the mechanical characteristics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".