Design and Specification Compilation of a Modularized Prefabricated High-rise Steel Frame Structure with Inclined Braces Part I: Integral Structural Design
Bibliographic record
Abstract
Modularized prefabricated steel structures have certain obvious advantages, i.e., rapid construction, industrial-scale production and pro-environmental aspects, and are the main method in industrialization of steel structures. Although applications of these structures have been reported all over the world, in most cases, the steel structural systems are only suitable for low-rise buildings, and their application in high-rise buildings is quite limited. This paper proposes a new type of modularized prefabricated high-rise steel frame structure with inclined braces. Based on the T30 hotel building, the mechanical properties, failure mode, failure mechanism and elastic-plastic development laws are investigated by using the elastic design of a structure under various load conditions, the analysis of the internal force and displacement responses under frequent earthquakes via the response spectrum method and linear time-history analysis, the static elastic-plastic pushover analysis under rare earthquake conditions. According to finite element simulations and testing, elastic and elastic-plastic structural design methods are proposed in this paper. This work provides an important reference for research and design of the same type of modularized prefabricated high-rise steel structures, and the design method has been compiled into design specification.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".