Mitigating torsional irregularity using cross laminated timber-reinforced concrete hybrid system
Bibliographic record
Abstract
Seismic torsional responses in buildings is a result of eccentricity in mass and stiffness distribution. Torsional irregularity is one of the major causes of severe damage and collapse of structures during an earthquake. In this study, effect of torsion on the structures is reviewed, the definition of torsional irregularity and the characteristic of the structure that leads to this type of irregularity is elaborated. The evolution of the methods to consider the effect of torsion in the National Building Code of Canada (NBCC) is reviewed and different methods to prevent torsional irregularity in the structures are discussed. Hybridization with Cross-Laminated Timber (CLT) is suggested as a new method to rectify the effect of torsional irregularity for different performance levels. Accordingly, the definition of hybridization and hybrid structure seismic behavior, CLT material specifications and CLT seismic performance is discussed. In order to evaluate the effect of CLT hybridization on buildings with torsional irregularity, a four-storey reinforced concrete (RC) structure with torsional irregularity is considered for Vancouver seismicity condition. SAP2000 software is used to conduct Linear Dynamic Analysis (LDA) and Non-Linear Time History Analysis (NLTHA) using eight different ground motion scaled to Vancouver design spectra. The effect of the CLT wall panel as shear wall on the in plane seismic base shear and inter-storey drift is shown using the linear and non-linear dynamic analysis. The result from the analysis compared to the code static values. The literature of Performance Based Seismic Design (PBSD) is reviewed. PBSD is used to determine the performance level of the original and hybrid building. The inter-storey drifts criteria defined in FEMA 356 guidelines is used for the purpose of NLTHA.
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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) | 0.000 | 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) | 0.001 | 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".