Seismic design and evaluation of self-centering modular rocking composite sandwich wall (SC-MRCSW) system
Bibliographic record
Abstract
With rapid population growth worldwide and increasing urbanization, there is a growing demand for infrastructure that can be constructed quickly, while maintaining sustainability and resilience. In order to enhance the seismic resilience and constructability, this thesis introduces a novel self-centering modular rocking composite sandwich wall (SC-MRCSW) system. SC-MRCSW system consists of steel plates in-filled with concrete, where the steel plates are tied using steel bolts. The steel plates reduce the need for concrete formwork which significantly reduces the on-site constriction time, making SC-MRCSW system a highly efficient structural system for high rise application. To improve the performance of the SC-MRCSW, novel self-centering friction dampers are added to the system to ensure SC-MRCSW system can dissipate earthquake energy and self center after strong earthquake shaking. This makes SC-MRCSW system highly resilient. This thesis focuses on the seismic design of SC-MRCSW system. This is achieved using the novel Equivalent Energy Design Procedure (EEDP). Detailed numerical model of the SC-MRCSW system was developed to simulate the non-linear dynamic response of the system. The results show that SC-MRCSW system is able to achieve superior performance at different levels of earthquake shaking intensities. This thesis shows that the proposed SC-MRCSW system can be used as a reliable seismic force resisting system and can be designed efficiently using EEDP.
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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) | 0.002 | 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".