Resilience based seismic design of CLT coupled walls and Glulam moment resisting frame system
Bibliographic record
Abstract
Seismic design philosophies have evolved significantly over the past several decades, shifting from life safety focused – prescriptive methods – towards approaches that also consider post-earthquake recovery, economic losses, and social impacts. This transition has led to the emergence of Resilience-Based Seismic Design (RBSD). While RBSD has been explored for concrete- and steel-based structural systems, its application to timber structures remains limited. Accordingly, this study develops a novel RBSD framework for a 20-storey timber building combining Cross-Laminated Timber Coupled Walls (CLTCWs) and a Glulam Moment-Resisting Frame (GMRF) to resist lateral loads. A baseline system is designed and assessed using FEMA P-58 methodology and the TREADS repair time model under multiple seismic intensity levels. Using this baseline, a Multi-Objective Optimization (MOO) framework is formulated with conflicting objectives: minimizing structural strength demands while maximizing its resilience. A dynamic deep learning-based surrogate model is trained to predict seismic performance across varying design parameters. Non-dominated Pareto-optimal solutions are obtained using a genetic algorithm and further evaluated through nonlinear time–history analyses. Results show that the optimized solutions achieve notable improvements in both structural efficiency and resilience performance compared to the baseline system. This research contributes a flexible and data-driven methodology for advancing the design of resilient, high-performance tall timber buildings. • A RBSD framework is proposed, employing a MOO approach that integrates deep-learning-based surrogate models with genetic algorithms. • The framework incorporates the FEMA P-58 methodology and TREADS repair time model, enabling comprehensive resilience. • The framework is applied to the design of a 20-storey innovative timber-based dual system system. • Through the RBSD process, optimal design parameters are determined, accounting for the complex interactions between the dual system components. • It provides a novel decision-making tool, enabling stakeholders to quantitatively balance seismic safety, economic losses, and functional recovery objectives within a unified framework.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".