Constitutive Model for Cemented-Soil Based on a Dynamical Systems Approach under Monotonic Loading
Bibliographic record
Abstract
Soil stabilization using cement is a widely adopted geotechnical technique for improving mechanical properties, such as settlement and shear strength.However, cemented soils exhibit unique behaviours, including post-peak strain-softening, that are not always captured by conventional models.Several constitutive model frameworks, such as the critical state models and Discrete Element Method (DEM), have been widely explored to simulate soil behaviour, but the Dynamical Systems Soil Mechanics (DSSM) framework, despite its potential, has been less extensively studied, especially in the context of cemented soils.In its current form, the DSSM framework does not adequately represent the behaviour of cement-treated soils.This study proposes modifications to the DSSM framework to incorporate strain-dependent cohesion, enabling it to simulate the degradation of cementation bonds under monotonic loading.The modified model introduces an exponentially decaying cohesion term to represent the progressive breakdown of interparticle cementation, ensuring compatibility with the original DSSM framework's dynamic equations.Validation was conducted using triaxial compression test data from literature, spanning different curing times and confining pressures (CP).The modified model closely replicates the stress-strain response, achieving high correlation coefficients (0.98-0.99) for experimental and predicted results.These findings establish the model as a computationally efficient and accurate tool for simulating cemented-soil behaviour.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".