Use of computer vision to analyze cyclic loads on the Guamo sand
Bibliographic record
Abstract
Some effects have been evidenced during several earthquakes around the world. For example, the liquefaction phenomenon, and structural collapse, among others. However, granular soil behaviour during this event has not been completely understood; this is proved by the number of investigations in physical models, constitutive models, and laboratory testing proposals about this topic. A question appears at this point: when do the effects take place? To cope with this issue, it is proposed to create an experimental model composed of a Single Degree of Freedom Oscillator (SDOFO) designed to try to assess the liquefaction. Likewise, the device could transmit cyclic loadings to the soil. This SDOFO was an electromechanical oscillator servo-controlled appropriated to manage the frequency of excitation and amplitude of the oscillatory movement. The measurement methodology was based on computer vision using Open CV by Python, which allowed measuring displacements and times of the SDOFO instead of using LVDT sensors located directly at the actuator; thus, computer vision permitted a “free movement” of the actuator similar to the actual behavior of buildings in seismic events. The samples required for this study were the Guamo sand collected in a natural deposit in Colombia (South America) and the Ottawa sand to compare results. A simple criterion was used to try to determine the possible liquefaction onset. It was defined based on the evolution of displacements relying on the number of cyclical loads. According to the results, it was able to define that the relationship between the excitation frequency, the amplitude of the oscillatory movement, and the number of cycles required to reach the possible liquefaction state was inverse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".