Lateral structural performance of Yingxian Wood Pagoda based on refined FE models
Bibliographic record
Abstract
This paper presents the structural system analysis,refined FE modelling,dynamic characteristics and lateral structural performance analysis of Yingxian Wood Pagoda.The pagoda can be regarded as a layered platform beam-column structure.Two refined FE models of the structure of Yingxian Wood Pagoda with and without considering damage were developed based on ABAQUS platform,respectively,in which the Dou-Gong was modeled with beamshort-column assembly and the column-frame joints were simulated with stiffness-equivalent elements.The lateral drift of the 1st story of the pagoda appeared as bending type while those of other stories showed as shearing type,since the mud wall strengthened the column frames of the 1st story.The lateral stiffness of the Pu-zuo layers in the apparent stories,the column-frames in hidden stories,the Pu-zuo layers in hidden stories and the column-frames in exposed stories decreased,sequentially.The lateral stiffness of the pagoda model with considering damage was averagely 33% lower than those of the pagoda model without considering damage.Under the frequent earthquake,the lateral drift ratios of several apparent stories exceed the elastic inter-story drift ratio limit.An index for evaluating the overturning of the pagoda was suggested,and the safety of Yingxian Wood Pagoda under earthquakes was assessed.Although Yingxian Wood Pagoda has not overturned under the frequent earthquake,there is still potential in some level to overturn under rare earthquake.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".