Stability Assessment of Concrete Gravity Dams via Multifidelity Surrogate Models
Bibliographic record
Abstract
When many repetitions of an expensive or time‐consuming analysis are needed, simplified models are usually adopted to reduce the cost. This is often the case with gravity dams under seismic load, especially if geometry variation needs to be considered. Deterministic analysis of dams is an important part of preliminary analyses but generally leads to overconservative designs. In recent years, many researchers have studied the potential of machine learning techniques to reduce the computational burden of dam assessment. However, generating the training dataset for a surrogate model based on high‐fidelity (HF) data can be expensive when a large set of uncertain parameters is considered. To address this issue, this study proposes the use of multifidelity surrogate (MFS) models. In this method, datasets with different levels of fidelity are combined to generate a highly accurate surrogate model at a lower cost. To illustrate this, the seismic behavior of a gravity dam is assessed by means of a HF nonlinear finite element model that considers geometric, material, and seismic uncertainties. In addition, five lower fidelity (LF) models are combined with HF samples to generate multifidelity models. The goodness of fit of the models and the computational time to produce the dataset are used to identify the combination that optimizes the MFS model performance. The results show that including medium‐ or low‐fidelity samples improves the predictive performance of a surrogate model and reduces its computational burden. The results also show that the data generation and the selection of the best LF model depend on the size of the HF dataset.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".