Strategies and Design Challenges for Topology Optimization of Ultra-High-Performance Fiber-Reinforced Concrete Precast Beams
Bibliographic record
Abstract
Topology Optimization (TO) is a mathematical method that searches for the ideal spatial distribution of materials based on the design domain, objective function, constraints and boundary conditions.There are different methods and softwares at hand, which can give distinct responses.Thus, the choice of the most appropriate algorithm and program is essential to provide a feasible solution.The aim of this work is to compare the results of the TO of simply supported rectangular Ultra-High-Performance Concrete (UHPFRC) beams, subjected to different static loads and spans ranging from 3 to 10m.The analyses include optimizing 3D parametric beams using Grasshopper's Algorithm-Aided Design (AAD), which is integrated with the Topos plugin, based on the SIMP model, and associated with Galapagos, a genetic algorithm plugin.This approach allows the automation search for the minimum volume of the rectangular beams.The second software used for TO processing is TOSCA, which is integrated with Abaqus/CAE, allowing optimization using the MIMP, RAMP and SIMP methodologies.Finally, the beams were processed in the Fusion software, from Autodesk Nastran In-CAD, which adopts the SIMP model.All the algorithms were used to provide the optimized geometry of the beams automatically to achieve the defined objectives of minimizing the volume and deformations.After the definition of the most suitable geometry, a non-linear analysis in Abaqus software is performed to check the maximum load bearing capacity (ULS) and the deflection (SLS).The results demonstrate a change in the failure mode, from flexural to a truss-like.Moreover, the optimized beams were able to maintain the initial load bearing capacity, without losing performance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".