Seismic Behavior of Multi‐Storey Volumetric Modular Buildings: Comparing Concentrically Braced Frames and Reinforced Concrete Shear Walls
Bibliographic record
Abstract
Abstract Multi‐storey modular buildings, constructed from prefabricated steel modules, are gaining popularity due to their efficiency, cost‐effectiveness, and reduced construction time. However, their seismic performance and stability remain key areas of research. While reinforced concrete shear walls are commonly used as seismic force‐resisting systems (SFRS), this study investigates the feasibility of using concentrically braced frames (CBF) as an alternative. Although CBF effectively resists lateral loads, they are susceptible to soft‐storey collapse under seismic excitation. This paper examines the lateral behaviour of two 12‐storey modular buildings, where steel modules serve as the gravity force‐resisting system (GFRS), and either CBF or reinforced concrete shear wall acts as the SFRS. Nonlinear finite element pushover analyses are conducted using OpenSees to evaluate key performance metrics, including global behaviour, inter‐storey drift, and shear distribution between the GFRS and SFRS. The findings highlight the potential for efficiently using CBF in multi‐storey modular steel structures while providing deeper insights into the seismic force distribution between the GFRS and SFRS. Notably, results indicate that the GFRS can carry a significant portion of seismic forces, particularly in the upper stories.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".