SEISMIC DESIGN OF IRREGULAR BUILDINGS: NONLINEAR ANALYSIS AND CASE STUDY OF 1568 ALBERNI, VANCOUVER.
Bibliographic record
Abstract
Nonlinear Response History Analysis (NLRHA) is an effective method to capture seismic response of high-rise structures with irregular gravity systems. This paper discusses the application of NLRHA for the design and analysis of a 43- story irregular high-rise reinforced concrete building located in BC, Canada, where seismic and wind demands are high. The lateral force resisting system (LFRS) consists of two centrally located reinforced concrete C-shape walls coupled with Steel Reinforced Concrete (SRC) coupling beams. Post-tensioned diaphragms connect the core to sloping columns that provide a curvy shape to the façade as the floor plate varies at each floor. The unique geometry of the building poses significant challenges for service seismic, service wind as well as design seismic and wind load cases. The magnitude of Gravity-Induced Lateral Demand Irregularity (GILD) is much higher than the limit prescribed by the National Building Code of Canada (NBC) near mid-height of the building. To reduce leaning deflection of the building under GILD, residual deflection under the design seismic load, and cracking in the core under service seismic and wind events, the core was reinforced with vertical post-tensioned tendons. A suite of 11 ground motions with and without vertical ground shaking components were selected and scaled to the target spectrum. The suite of ground motions includes crustal, subcrustal, and subduction ground motions that are consistent with the seismicity of Southwest BC. The NLRHA was carried out in CSI PERFORM-3D. The NLRHA results were compared with acceptance criteria set in PEER TBI II in order to meet target reliability thresholds specified in NBCC 2020 and ASCE 7-22. In addition, all diaphragms were modeled in VecTor 2 to accurately estimate their in-plane deformation under GILD demands. Finally, a staged construction analysis was carried out in CSI ETABS to estimate the long-term deflection of the building under GILD
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".