Development of RISHA Precast Concrete System for School Buildings Function in Indonesia’s Severe Earthquake Regions
Bibliographic record
Abstract
This paper aims to convey the results of the development of dry joint modular precast concrete system for school buildings function in the severe earthquake areas of Indonesia with values of Ss ≥ 0.911 and S1 ≥ 0.391.The numerical model of the structure was developed by utilizing the partial experimental test results from various type of structural member joints of the system.From the results of these tests, the nonlinear behavior of each type of structural joints in the form of a moment vs rotation curve is obtained to be implemented in the structural model using nonlinear link elements in order to represent the nonlinear behavior of the structure.The contribution of strength and stiffness of the infilled masonry walls using lightweight Autoclaved Aerated Concrete (AAC) brick is modelled through nonlinear strut elements whose behavior has been calibrated with experimental test results.A pushover analysis in the numerical model was carried out to obtain the system capacity curve of the proposed building structure with the typology of the school building.The results of performance point evaluation of the structural capacity curve using methods of ATC-40 and FEMA 440 in various loactions of severe earthquake areas in Indonesia show that the performance of Damage Control (DC) was achieved by providing a horizontal steel frame at the topmost elevation of the building structure in order to obtain the diaphragm behavior in each structural module.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".