A Computationally Efficient Beam Element for FEM/DEMSimulations of Structural Failure and Collapse
Bibliographic record
Abstract
The research here presented has employed the newly evolved combined finite-discrete element method in the development of novel numerical solutions for the analysis of failure and collapse of reinforced concrete structures under hazardous loads such as those arising from blast loading. Recent bomb explosions have made engineers more aware of the need to develop efficient computational tools as a viable cost effective means of analysing the failure, fracture and collapse of structures and structural elements. The first step to achieving this was the study of the structural response, failure, and collapse of individual structural elements. Thus the research in this area is taken further by using numerical solutions to study the behaviour of reinforced concrete beams to the point of failure. The results are implemented into the combined finite-discrete element method through a novel computationally efficient two noded beam element. Numerical integration across the cross section of the beam element is applied to facilitate the application of non-linear constitutive laws for both steel and concrete for the case of multi-axial bending coupled with axial force. The accuracy of this new element is tested and validated under both static and dynamic loading situations using analytical solutions together with experiments undertaken at the University of Alberta and The Swiss Federal Institute of Technology, Zurich. The proposed element has the advantage of reducing the size of the problem by fifty percent through elimination of the rotational degrees of freedom using static condensation. The new element, enables the same finite element mesh to be used for the discretised contact solutions, thus further reducing the CPU time required. When implemented into the combined finite-discrete element method, the proposed numerical solution also takes into account contact-impact and inertia effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".