Evaluation of Pedestrian-Induced In-Service Building Floor Performance Based on Short-Term Monitoring
Bibliographic record
Abstract
Pedestrian-induced vibration (PIV) is often the most persistent issue affecting the floor serviceability of buildings. These vibrations may cause discomfort to occupants and may also adversely affect the performance of sensitive equipment residing on the floor. Resolving floor vibration problems in built structures often requires costly mitigation measures. Using monitoring techniques, a floor’s response to PIV can be evaluated. The purpose of long-term floor monitoring is to have a comprehensive insight into the vibration levels. Due to the associated costs and challenges, long-term monitoring is not common. The alternatives, short-term monitoring and controlled walking tests, may not reflect the actual vibrations of the floor but are easier to perform. By using confidence interval (CI) analysis, CI width analysis, and the Kullback–Leibler divergence (KLD) method to evaluate measured PIV from a floor, this study proposes a methodology for obtaining the sufficient short-term monitoring duration (MD) that is required to evaluate the long-term measured PIV with acceptable accuracy. Also, the appropriate percentile(s) for evaluating floor performance is investigated. Two methods are discussed for deciding which percentile to use when evaluating floor performance. The first method is based on selecting the probability of non-exceedance (PONE) according to the PIV guidelines, and the second method is based on the definitions of the types of vibration based on ISO 10137 curves. The sufficient MD is obtained from the relative error calculation of the results. The results of this research provide a more realistic and improved methodology for analyzing the vibration performance of floors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".