Analytical and Numerical Modelling of Debris Impact Events on Columns
Bibliographic record
Abstract
Post-disaster surveys of tsunamis have emphasized the need for an in-depth understanding of debris loading. Until now, empirical formulas used to estimate debris impact loads are based on single-degree-of-freedom (SDOF) models. However, the validity of these SDOF models to estimate debris impact loads has not been studied extensively. This study investigates the validity of using a SDOF model to predict debris impact forces by comparing its force response to experimental data and a multiple-degree-of-freedom (MDOF) model developed. Additionally, a comparative analysis was conducted to assess the provisions on debris impact loads in Chapter 6 of ASCE 7-22 against these alternative methods. The MDOF method was shown to model accurately the experimental force response data, while all other methods for estimating debris impact loads overestimated the force response in both magnitude and frequency. Furthermore, the impact loads generated by the MDOF model proved to be longer in duration but smaller in magnitude than loads generated using the SDOF model and Chapter 6 of ASCE 7-22. In addition, a performant numerical model was developed to simulate single and multi-debris transport and impact loads on a column. The dynamic numerical model was developed within the general-purpose finite element program LS-DYNA. Inside this modelling framework, the Arbitrary Lagrangian-Eulerian (ALE) method was used to simulate dam-break wave generated debris impact loads onto the column. The model accurately replicated the water surface elevations, hydrodynamic forces, debris transport, and debris impact forces presented in Stolle et al. (2019) and Stolle et al. (2020b). The model’s ability to simulate debris impact events demonstrates its potential as a valuable tool for designing and evaluating critical infrastructure’s resilience against extreme coastal inundation events, such as tsunamis.
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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".