A Two-Phase Genetic Algorithm for Simultaneous Dimension, Topology, and Shape Optimization of Free-Form Steel Space-Frame Roof Structures
Bibliographic record
Abstract
The objective of this work is to study the effects of geometry on structural performance of free-form steel space-frame roof structures and to optimize the structures without compromising overall architectural forms.Minimum weight optimization is performed to better study the effects of geometric alterations on overall structural performance.The intent is to achieve a strong optimum shape with superior load-carrying capacity allowing for the smallest and lightest structural members to be used.A two-phase genetic algorithm (GA) is developed to perform minimum weight design of the roof structures which consist of rectangular hollow structural sections (HSS).The new methodology is applied to two example roof structures subjected to the AISC LRFD code (AISC, 2005) and ASCE-10 snow, wind, and seismic loading (ASCE, 2010).Both are train station roofs for the Ottawa Light Rail Transit (OLRT) system to be built in Ottawa, Canada, in 2018.The structures are made up of a diamondshaped grid pattern and their members are subjected to torsion in addition to bending and axial forces.The GA was developed to perform simultaneous dimension, topology, and shape optimization and resulted in final designs which are 22% and 24% lighter than the initial designs created in a design office for the two roof structures.This global optimum solution was achieved in less than 19 hours on a standard workstation machine with a 2.83 GHZ dual core processor, a relatively short amount of time considering the complexity of both the structures and the optimization problem.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".