Deep learning-based surrogate model to estimate scour around slab-on-grade foundations subjected to flooding conditions
Bibliographic record
Abstract
Residential foundations in coastal areas are susceptible to loss of soil support due to erosion and scour during storm surge events. The current methods to address these issues are largely based on empirical evidence and lack sensitivity to site-specific conditions. Thus, a research study was designed and performed to develop a computationally efficient deep learning-based surrogate model to estimate scour around slab-on-grade foundations on sandy soils over a wide range of flooding conditions. A fully coupled 3D numerical model, calibrated using published laboratory test studies, was used to simulate scour around slab-on-grade foundations. This model was used in conjunction with a space-filling experimental design to generate sample points required to develop a surrogate model. Two deep neural network (DNN)-based surrogate models were developed and tested for predicting scour for varying flow and soil conditions. The DNN-based surrogate model, augmented with modified loss function to penalize un-physical predictions, was shown to achieve the best performance among the studied methods. Monotonicity analysis on the model showed that flood velocity, duration, and grain size properties of soils were found to have a major influence on the predicted maximum scour depth as compared to flood depth. The surrogate model developed can serve as an aid in achieving improved storm surge hazard preparedness, as well as proactive planning for post-hazard recovery efforts in vulnerable coastal communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".