Bibliographic record
Abstract
The past 20 years have witnessed remarkable advances in the digital design of structures. This includes the ability toimaginea diverse range of chaotic, curved and parametric structures. Where early software offered no interoperability between architectural design, engineering and fabrication software and the associated technical requirements, more recently the level of interoperability has soared. At this point Cloud based systems permit architects, engineers and steel fabricators to simultaneously create a comprehensive set of documents for structures. This level of communication has the potential to speed up the design and detailing process as well as minimize conflicts in all aspects of the construction process. Many physical tools have been invented that are increasingly being employed to automate the processes used in the fabrication of more normative, orthogonal, structures. However, to a certain extent the actual fabrication of the steel used in complex structures has not changed appreciably over the same time. Although some computer assistance is used to cut complex shapes (particularly plate material) and control repetitive procedures such as the drilling of holes, the majority of the process has remained a craft that is carried out by the ironworker. This means that the success of the project still largely rests on the expertise of the welder and the judgment of those involved in the erection process. As computation methods evolve at such a rate as to make much printed discussion of them rapidly out of date, this paper instead looks at the important lag between the design and fabrication of complex steel structures. Highlighted are issues of the increased importance for tight tolerances, achieving uniformity, and team coordination/communication in this yet largely craft based system and important accommodations that are required to ensure the proper fabrication and erection of Architecturally Exposed Structural Steel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.087 | 0.016 |
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".