RELATION BETWEEN MONITORING AND DESIGN ASPECTS OF LARGE EARTH DAMS
Bibliographic record
Abstract
Safety of earth dams depends on the proper design, construction, and monitoring of actual behaviour during the construction and during the operation of the structure. The geotechnical and geodetic monitoring besides providing a warning system in case of an abnormal behaviour of the dam, may be used as a tool for a verification of design parameters where geotechnical parameters are of the highest importance. By comparing results of monitoring measurements with a prediction (deterministic) model of deformation, one may determine and explain causes of deformation in a case of unexpected behaviour of the investigated object and its surrounding. Modelling of deformation of earth dams is a complex process in which one should consider the nonlinear behaviour of the construction material, interaction between the structure and the underlying soil and rock strata, influence of water load on the structure and on the foundation bedrock, and the effects of water saturation. The deformation process can be simulated (predicted) using, for example, the finite element method with the hyperbolic model of the nonlinear behaviour of the material. Due to the uncertainty of the model parameters, careful monitoring of the dam and its surroundings are required in order to verify and enhance the model. Thus, the role of monitoring becomes much broader than just the conventional determination of the status of the deformable object. In addition, with properly designed monitoring surveys, one may also determine the actual deformation mechanism. The discussed problems are illustrated by three types of earth dams located in California, USA, and in Quebec, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".