Quantify seismic reliability of steel moment frame structures\\ with numerical procedures
Bibliographic record
Abstract
Quantifying the reliability indices of structures under earthquake loading is traditionally considered to be challenging, especially when the nonlinear structural behaviour needs to be considered. With the increasing popularity of high-performance computer clusters, it is feasible to use detailed numerical procedures to quantify seismic safety margins of steel moment resisting frame (SMRF) structures under various sources of uncertainties. Two seismic reliability methods are used to examine the interaction of uncertainty from ground motions and intensity. One is a numerical integration procedure for the traditional method. The other is the Monte Carlo simulation. These methods produce cumulative probability distribution curves that can retain the accuracy of results from nonlinear dynamic analysis. These methods are applied to two SMRF structures to investigate their probabilistic behaviour with their uncertainties from earthquake loads and seismic weights. The global reliability indices of the structures are found to be between 2.5 and 2.1 under the maximum considered earthquake (MCE). When an MCE occurs, the conditional reliability indices of the structures range between 1.4 and 1.0. The results indicate that both methods can be used to accurately quantify the reliability of SMRF structures. The results also show that some conditional probability distributions may not be well-represented by simple equations with their parameters calibrated from data-fitting techniques. The results also prove that the discussed methods and numerical procedures can be further used to accurately quantify probabilistic seismic behaviour of other structures toward the community resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".