New methodology to generate floor design spectra (FDS) directly from uniform hazard spectra (UHS) for seismic assessment of non-structural components (NSCs) of buildings
Bibliographic record
Abstract
Achieving global good seismic performance of a building as required in modern building codes is contingent upon maintaining the integrity and functionality of its structural system as well as its Non-Structural Components (NSCs).Experience of past earthquakes has shown that many buildings have suffered from the failure of NSCs which caused life safety hazards, costly property damages, and significantly impacted the building functionality while their structural systems have performed satisfactorily.Avoiding these undesired consequences is of tremendous importance particularly in post-disaster buildings that must remain operational during and after earthquakes.That is why the rational assessment of seismic performance of NSCs has been the focus of many researchers during the last few decades with a focus on performance.Most recent editions of building codes incorporate empirical equations for seismic design of NSCs which are, for the most part, based on past experience and engineering judgment, rather than on objective experimental and analytical results.The lack of significant advances in design code provisions may be attributed partly to the fact that the previously developed analytical methods are too cumbersome to be employed in the design of ordinary NSCs (and their connections) housed in conventional buildings.As an effective solution to these problems, an original approach is developed and introduced in this thesis to generate Floor Design Spectra (FDS) directly from Uniform Hazard Spectra (UHS) specified in building codes.Generated FDS play the same role as UHS for structural components and can be used as a simple, fast, and reliable tool for seismic assessment and analysis of NSCs particularly in existing post-critical buildings.To develop and validate the proposed method, Ambient Vibration Measurements (AVM) data pertaining to 27 existing
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".