Predictive numerical modeling of the behavior of rockfill dams
Bibliographic record
Abstract
Choosing an appropriate soil constitutive model is one of the most important elements of a successful finite element or finite difference analysis of soil behavior. There are several soil constitutive models; however, none of them can reproduce all aspects of real soil behavior. In this research, various constitutive soil models have been studied through triaxial and oedometer tests. Two finite element software applications, namely, Plaxis and Zsoil, were used for numerical analysis. Subsequently, the numerical simulation values were compared with experimental test results to determine which of these constitutive soil models obtained the closest results to the experimental data. \n \nThe main focus of the study is the comparison between the measured data from monitoring instruments and the numerical analysis results of the Dam-X. Dam-X is an asphaltic core rockfill dam constructed on a River in the North Shore region of Québec. The rockfill dam behavior was analyzed numerically using finite element programs for different stages of construction and after impoundment. The measured data from monitoring and numerical analysis results represent the appropriate response of the Dam-X. The aim of this study is to evaluate the performance of numerical solutions by considering various constitutive soil models, namely, the Duncan–Chang, MC, and HS models. Comparisons were conducted to determine which of these constitutive soil models obtained the closest results to the measurements.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".