Computer Aided Design And Analysis Of RC Frame Buildings Subjected To Earthquakes
Bibliographic record
Abstract
Computer use in structural analysis and design dates back a number of decades. As computer processors become more powerful, the scope of computer aided design and engineering expands. However, many specialized analysis tools in structural engineering lack the flexibility in user interface and analysis process automation that is usually assumed in computer aided design and engineering. This paper focuses on the earthquake resistant design of reinforced concrete building frames and IDARC2D, a computer program that facilitates seismic response analysis of RC frame buildings. The paper presents a user interface design and a scheme for automating the analysis process for large scale simulation for evaluating the seismic performance of RC building frames subjected to earthquake ground motion. Such large scale simulation produces a huge amount of data that needs to be post processed in order to extract meaningful information about the behaviour of a building under earthquakes. The paper also discusses the development of such post processor. As a case study, a six story RC frame building designed based on the NBCC 2005 seismic provisions is analyzed using the software tools discussed here. The building is assumed to be located in Vancouver in western Canada. The seismic provisions of NBCC 2005 are different from those in the earlier edition of the code. NBCC 2005 presents an objective-based format where the design is achieved through the attainment of acceptable solution, rather than just satisfying the minimum requirements. For earthquake resistant design, evaluation of the seismic performance of buildings is essential to determine if an acceptable solution in terms of performance is achieved. The seismic performance of the buildings has been evaluated using nonlinear static and dynamic analysis. A set of eight simulated ground motion records which are compatible with the seismic hazard spectrum of Vancouver has been used in the dynamic analysis. The advantages of the tools developed herein are demonstrated along with a summary of the results for the selected building.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".