Lateral vibration data of an 18-story timber-concrete hybrid building obtained by on-site vibration tests
Bibliographic record
Abstract
The dataset provided in this paper includes the lateral vibration data obtained by on-site vibration tests on an 18-story timber-concrete hybrid building, which was completed in 2017 and is located within the University of British Columbia campus in Vancouver, Canada. Aiming at evaluating vibration properties such as natural periods, damping ratios, and mode shapes, microtremor measurement (i.e., ambient vibration test) and human-powered excitation tests were conducted from March 27th to 29th, 2023. Lateral velocity data of the building were collected using eight servo velocity sensors, type VSE-15D, and a data acquisition system, type SPC-52, composed of an A/D converter and a DC amplifier. The voltage data picked up by the sensors was first converted to digital data and then transferred to the hard disk drive in a laptop through Ethernet. In the microtremor measurements, the lateral vibration in the longitudinal and the transverse directions of the test building were measured with three types of locations of the sensors in order to evaluate the vibration behavior along the height of the building and its torsional behavior. The human-powered excitation tests were carried out by pushing walls by eight persons in accordance with the fundamental frequency in the transverse direction, aiming at obtaining a larger amplitude than with microtremor. The dataset is useful to deepen the understanding of the dynamic properties of tall timber-concrete hybrid buildings and encourage reasonable structural design and health monitoring of tall mass timber buildings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".