Bridging perspectives: a foundational dataset for the empirical aesthetics of bridge design
Bibliographic record
Abstract
People readily recognize London when they see Tower Bridge, San Francisco by the Golden Gate Bridge, and Sydney when they see the Sydney Harbour Bridge. These bridges have emerged as iconic landmarks that shape their city’s skyline by virtue of their aesthetic qualities. Bridges can also elicit a neutral aesthetic response and, sometimes, can be regarded as downright ugly. Bridges are designed as public infrastructure, which often shape their surroundings for centuries. Nonetheless, little is known about what shapes the aesthetic appeal of bridges. Here we explore how aesthetic judgements of bridges relate to engineering and design features. Our dataset comprises 318 images of 118 bridges from around the world, rated by 200 participants for aesthetic pleasure, interest, complexity, and safety. A civil engineering team annotated each image for type, depth, material, apparent age, and aesthetic premium. Using Factorial Analysis of Mixed Data (FAMD), we found two significant dimensions. The first “aesthetics” dimension shows strong correlations among aesthetic, complexity, and interest ratings and is connected to bridge type. The second “safety” dimension relates subjective ratings of safety to bridge age and material. Analysis of visual features of bridges, using the Mid-Level Vision (MLV) Toolbox, shows that contour length is a predictor of both bridge type and the aesthetic, complexity, and interest ratings. For example, truss bridges, made up of several interconnected beams, are represented by many short contours and are generally rated as more complex, interesting, and pleasing. On the other hand, the visually simple slab and girder bridges are often represented by a few long contours and are rated as uninteresting and not aesthetically pleasing. Our study offers the first attempt to systematically collect and analyze subjective ratings of bridge aesthetics, paving the way for empirically supported decisions for the design of bridges and, potentially, other public infrastructure projects.
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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 | 0.001 | 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 | 0.000 |
| 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 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".