Development of Probabilistic Framework for Performance-Based Seismic Assessment of Structures Considering Residual Deformations
Bibliographic record
Abstract
Recently, the importance of considering residual (permanent) deformations in the performance assessment of structures has been recognized. Advanced structural systems with re-centering properties as those based on unbonded post-tensioning tendons are capable of controlling or completely eliminating residual deformations. However, for more traditional systems, which count for the vast majority of buildings, residual deformations are currently considered an unavoidable result of structural inelastic response under severe seismic shaking. In this article, a probabilistic framework for a performance-based seismic assessment of structures considering residual deformations is proposed. The development of a probabilistic formulation of a combined three-dimensional performance matrix, where maximum and residual deformations are combined to define the performance level corresponding to various damage states for a given seismic intensity levels, is first presented. Combined fragility curves expressing the probability of exceedence of performance levels defined by pairs of maximum-residual deformations are then derived using bivariate probability distributions. The significance of evaluating and accounting for residual deformations within a Performance-based Earthquake Engineering (PBEE) approach is further confirmed via numerical examples on the response of Single Degree of Freedom (SDOF) systems, with different hysteretic behavior, under a selected suite of earthquake records. Joined fragility curves corresponding to various performance levels, defined as a combination of maximum and residual response parameters, are derived while investigating the effects of hysteretic systems and strength ratios. It is observed that stiffness degrading Takeda systems result in lower residual deformations than elasto-plastic systems and show lower probability of exceeding a jointed maximum-residual performance level. For a chosen performance level, Takeda systems with higher strength ratios show better performance, particularly with lower intensity of excitations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".