Seismic loss and resilience assessment of CLT coupled walls and Glulam moment resisting frame system
Bibliographic record
Abstract
Evaluating the seismic resilience of tall buildings, particularly in high-seismic zones, is critical to minimize economic losses and post-earthquake downtime. This study assesses the seismic loss and resilience of a 20-storey dual timber-based structural system combining Cross-Laminated Timber Coupled Walls (CLTCWs) and a Glulam Moment-Resisting Frame (GMRF). Using the FEMA P-58 methodology, intensity- and time-based seismic losses — including repair costs, downtime, and functional recovery — are quantified. Additionally, the TREADS framework is utilized to estimate building’s recovery time, shelter-in-place and functionality, considering impeding factors. The results indicate that, compared with the GMRF components, the CLTCWs sustain relatively higher damage levels; however, the system’s overall resilience is enhanced by the replaceable components in the CLTCWs, which facilitate efficient post-earthquake repairs. GMRF components, particularly beam–column and column-base joints, exhibit lower damage states, contributing to the system’s robustness. Non-structural elements, such as wall finishes, traction elevators, and partitions, contribute to repair costs. Recovery time estimates highlight the influence of external impeding factors, particularly financing and resource availability, on post-earthquake building usability. The findings emphasize the importance of integrating resilience-based seismic design strategies in tall timber construction and highlight areas for future research, including refining fragility models for mass timber components. • Proposed a resilience assessment framework, integrating the FEMA P-58 methodology and TREADS repair time model. • The framework is utilized to evaluate the seismic loss and resilience of an innovative timber-based dual system. • Defined key building information models, including reconstruction cost, reconstruction time, component damage fragility and consequence functions, applicable to mass timber systems. • Demonstrated the effectiveness of replaceable components in enhancing seismic resilience and post-earthquake recovery of systems. • Identified critical building components that need consideration when applying a resilience-enhancement or design to the system.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".