Multi-objective optimization based seismic design of CLT coupled wall and Glulam Moment Resisting Frame System
Bibliographic record
Abstract
This research develops a timber-based structural system combining Cross-Laminated Timber Coupled Walls (CLTCWs) and Glulam Moment Resisting Frame (GMRF) systems. The CLTCWs system features CLT balloon shear-walls coupled with energy dissipative replaceable beams and hold-downs made up of buckling-restrained braces (BRB), while the GMRF provides additional ductility and energy dissipation through beam–column joints with steel dampers. A multi-objective optimization (MOO) framework is developed to design a typical 20-storey CLTCWs-GMRF system. Initially, a baseline system is developed using the linear static procedure, and its performance is evaluated through nonlinear analysis in OpenSees under 50 bi-directional ground motions representative of Vancouver’s seismic hazard. Using the analysis results, key design variables, objective functions, and constraints are identified to generate samples for optimization. A deep learning-based surrogate model is trained and a genetic algorithm is utilized to optimize the system, targeting specific performances related to damage states of structural components. The results demonstrate the feasibility of the developed system, highlighting its potential as an alternative tall timber solution, and the effectiveness of the proposed MOO-based design framework in achieving a high-performance resilient system. • A dual system, combining CLT Coupled Wall & Glulam Moment Resisting Frame is proposed. • A multi-objective optimization based seismic design framework is introduced. • A 20-storey CLTCWs-GMRF system is designed for demonstration purposes. • The interaction between coupled walls and moment resisting frame systems is studied. • Optimal coupling beam force profile is obtained in the presence of interacting frame.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".