Using Parallel Computer Systems to Examine Seismic Reliability of Structures
Bibliographic record
Abstract
Structural response under seismic loadings is typically nonlinear and related to many factors, such as structural configurations, material properties, occupancy loads, earthquake hazards and incomplete knowledge of the system.As all these factors have their sources of uncertainties, structural response under seismic loading has its probabilistic nature.Therefore, the random variable for any structural demand follows a multivariate probability distribution over the integration domain defined by the limit states.Examining the probabilistic behaviour of structures under earthquake loadings has to consider the sources of uncertainties from all factors.It is also known that numerical methods, such as the finite element method, are commonly used to predict nonlinear structural response.The probabilistic structural demand is a discrete probability function of its related variables.In order to examine seismic risks and mitigate potential damages to structures, it is important to accurately quantify seismic reliability of structures.The traditional seismic reliability analysis uses approximate algebra equations with parameters obtained from aggregation of data points of dynamic analysis, which may not be able to produce accurate results.In this paper, probabilistic seismic demands are solved with numerical procedures of the traditional SAC method and the Monte Carlo simulation.These methods rely on the results from repeatable nonlinear dynamic analyses, which were traditionally considered to be a bottle-neck due to limited computing resources.The recent progress in parallel computing technology and open-source software has made such scientific computation affordable for the engineering community.Two parallel computer systems were used to analyze seismic reliability of the structures.One system is based on multiple personal computers in typical computer labs.The other system is to use high performance computer clusters.Both systems were applied to analyze a two-storey wood frame building and a three-storey steel moment building, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".