Structural performance of bolted HSS-to-HSS connections with reinforcement technique
Bibliographic record
Abstract
Modular houses play a crucial role in addressing housing shortages, especially in emergencies and remote regions. This study examines the mechanical performance of three reinforcement techniques for Hollow Structural Section (HSS) connections in panelized modular houses: stiffeners beneath the extended plate, concrete filling within the HSS column, and a combined technique integrating both methods. A total of 162 finite element models were created via LS-DYNA software and validated using experimental results. This analysis focuses on ultimate moment capacity, ultimate rotation, failure modes, and the effectiveness of these reinforcement techniques in enhancing connection rigidity. The results demonstrate the significant influence of plate thickness, bolt arrangements, and reinforcement techniques on connection performance. Plates with a thickness of 8 mm are prone to premature tensile rupture, which can be mitigated using stiffener reinforcement. For the AR1.5 bolt arrangement, concrete reinforcement proved most effective, increasing capacity by 1.45 times the plastic moment, while stiffener reinforcement was the least effective. In contrast, for the AR0.6 arrangement, stiffener reinforcement induced local buckling in the column, whereas concrete reinforcement prevented it but increased ultimate rotation. A combination of reinforcement techniques is recommended for optimal performance, achieving the highest capacity of 2.2 times the plastic moment of the beam and reducing ultimate rotation by 76 % compared to concrete reinforcement alone. Design charts are proposed to predict failure modes by altering design parameters and are validated with tested specimens. The findings provide valuable insights for designing more efficient HSS connections in modular construction, improving structural integrity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".