Numerical Simulation of the Seismic Response of Steel X-Braced Frames with Single Shear Bolted Connections
Bibliographic record
Abstract
The paper presents a detailed numerical model that was developed using the OpenSees platform to predict the seismic response of X-braced frames in multi-storey building steel structures. The bracing members are HSS members with slotted-HSS bolted-plate connections, as commonly used in practice. Recent tests of full-scaled X-bracing systems designed using traditional methods have revealed that buckling of the compression braces is affected by the flexibility the connection components, which reduces the compressive resistance and energy dissipation capacity of the compression bracing members. This may also be a concern for this seismic force resisting system as excessive inelastic demand may develop in the brace connections and lead to premature failure in connections. Design methods have recently been proposed to increase the buckling strength of connections and mitigate these detrimental effects on the structure seismic performance. The validation of the proposed numerical model against test data is presented in the paper. The model can reproduce connection instability failure modes for both single shear and double shear connection configurations. The influence of the proposed connection strengthening schemes can also be investigated with the proposed model. The second part of the paper presents nonlinear time history analyses performed to study the seismic performance of prototype building structures designed according to the 2010 National Building Code of Canada and the CSA S16-09 steel design standards. The structures have two stories in height and are located in eastern and western regions of Canada. Two types of connections are investigated: single-shear-lap and double-shear-lap connections. The connections are designed using both the traditional and the proposed design methods for enhanced seismic behavior. The analyses are then used to compare the structure performance for both design approaches. Key behavioral characteristics such as story drifts and the loads delivered to the secondary components are used as measures to verify performance satisfaction. When connection buckling is inevitable, a parametric study is accomplished to determine the minimum connection plate thickness required to achieve a desirable response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".