Acquisition And Modeling Of Conceptual Structural Design Knowledge
Bibliographic record
Abstract
The goal of this research is to provide knowledge-based computer support for conceptual structural design through design suggestions and alternative evaluations. Rules-of-thumb from experience and generalized heuristic knowledge are mainly used. A knowledge acquisition process was performed from available literature and through interviews with two experienced structural engineers. For the interviews, design situations were simulated in which the engineers were videotaped while designing and thinking aloud. From these interviews, rules-of-thumb were obtained and a conceptual design process, established in advance, was validated. Additional knowledge, not available in the literature, was obtained through direct questions to engineers. The knowledge modeling is based on the technology nodes paradigm by which the engineer controls the design process and is allowed to backtrack to previously made decisions. Interaction is provided with a component called StAr (Structure-Architecture) that supports conceptual structural design through geometrical reasoning, based on a representation model that integrates architectural and structural entities. This interaction will permit the engineer to combine knowledge with geometric and functional architectural and structural concerns. A knowledge-based prototype will be implemented in Java. An envisioned interface for this prototype is presented in this paper.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".