Image‐based structural analysis for education purposes: A proof‐of‐concept study
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In civil engineering education, hand‐drawn sketches of structural systems are commonly used for response analysis. Based on these sketches, students may determine the response of the structures either by hand calculations or via some specialized software through the construction of finite‐element models. The former method is prone to errors, while the latter method could be time‐consuming for inexperienced students. It would be convenient if the information within the hand‐drawn sketches could be automatically converted into computer‐recognizable objects for further structural analysis. To address this issue, a novel method entitled Image‐based Structural Analysis (ISA) is proposed as a proof of concept to determine the response of a linear‐elastic structure directly from the image of a hand‐drawn sketch. A selective search algorithm and a deep convolutional neural network are adopted to detect relevant objects in the images. Based on the bounding boxes and the classes of the objects, a finite element model is constructed for further structural response analysis. This study demonstrates the proposed ISA method via several hand‐drawn beams under loadings. Results show that the proposed method, which consists of a combination of artificial intelligence and semantic rules, can produce, and analyze structural models from hand‐drawn sketches.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it