A Review of Structural Health Monitoring of a Football Stadium for Human Comfort and Structural Performance
Bibliographic record
Abstract
Stadium structures may suffer from vibration serviceability problems due to light weight and rapid constructions as well as considerations such as improved line of sight and increased capacity. In this context, Structural Health Monitoring (SHM) data can be implemented to track and evaluate performance of such structures during different events. This paper presents findings from a Structural Identification (St-Id) implementation to a football stadium to evaluate the structural performance by means of a detailed Finite Element (FE) model validated using experimental data. The stadium was monitored for three years to determine the vibration levels during different games and different events, e.g. goals, interceptions and playing a particular song. It is observed that certain events and long periods of playing particular songs generate vibration levels that create uncomfortable situations for the spectators based on the design codes. Laboratory studies were conducted to determine the forcing functions experimentally due to jumping with the rhythm of a song that was often played in the stadium. The FE model of the stadium was developed and validated using the modal analysis results from the ambient vibration data. The experimentally obtained loading functions were used with the FE model to simulate the behavior under spectators' loading.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".