PROBABILISTIC MODELING OF ABOVEGROUND STORAGE TANKS UNDER SURGE AND WAVE LOADS
Bibliographic record
Abstract
This study presents the development of probabilistic models to assess the structural performance of a typical aboveground storage tank (AST) subjected to storm surge and wave loads. First, a finite element model is developed and validated against experimental results to determine hydrodynamic loads on the AST. This finite element model is then employed to derive a regression model of the hydrodynamic loads across ranges of surge and wave parameters using an Artificial Neural Network. This regression model is used as a surrogate of the finite element model to facilitate the investigation of the structural behavior of the case study AST. Finally, the buckling behavior of the AST and the stability of the tank to dislocation (uplift, overturning, or siding) are assessed for various AST modeling parameters and load conditions in order to develop fragility models. Two distinct fragility models are derived, one for dislocation and one for buckling. Key insights on the influence of surge and wave loads are obtained from these models. Results indicate that wave loads and hydrodynamic effects are significant, and neglecting them could underestimate the probability of dislocation or buckling of the AST by up to 30%. Overall, this paper proposes a rigorous yet efficient methodology for the fragility modeling of ASTs during storm events and opens the path for future investigations of the performance of ASTs with a range of design details and exposure conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".