Using ambient vibration measurements to generate experimental floor response spectra and inter-story drift curves of Reinforced Concrete (RC) buildings
Bibliographic record
Abstract
Achieving the 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. Avoiding these undesired consequences is of great importance particularly in post-disaster buildings that have to remain operational during and after earthquakes. In spite of advances in seismic analysis of NSCs, there is still a need for a simple, practical and yet reasonably accurate approach for seismic design of NSCs. To gain acceptance in practice, this approach must be capable of circumventing the shortcomings of existing analytical approaches as well as of current building code provisions. This study proposes an original experimental approach to generate the Floor Response Spectra (FRS) and Inter-Story Drift (ISD) curves based on Ambient Vibration Measurements (AVM) in buildings. These outputs provide robust tools for the seismic evaluation of NSCs. This paper presents the application of the proposed method to a RC building. It addresses the dependence of the output results on NSC response parameters such as NSC location in the building, NSC dynamic properties (internal damping and natural periods), and natural periods of the building. The comparison of the results with the NBCC 2015 (National Building Code of Canada) provisions for NSCs shows that these empirical recommendations typically underestimate the seismic demands on NSCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".