Rocking of single-layer armour units measured by embedded sensors
Bibliographic record
Abstract
Single layer randomly placed armour units are used in many rubble mound breakwaters around the world. For these armour layers first extraction of units starts at high loads and can then progress quickly. Before the first extraction of a unit, typically no quantitative description of damage can be given. But additional to extraction, breakage of armour units due to rocking could be a major damage mechanism. This paper treat novel embedded Rocking Sensors. The technique is used to obtain the first measurements of rocking-impact velocities of single-layer units. They are also the first tests where the instrumented units can naturally move with the compacting layer during storm build-up. Physical model tests were performed on an armour layer with XBloc units. With 8 to 10 instrumented units per test run, in total 640 single measurements of the rocking motion of a unit during a 1200 wave test run were obtained, for three water levels and five wave heights. From the Rocking Sensors the number of impacts and rotational impact velocities were obtained. From an image analysis the along-slope settlement of the units during the tests was quantified. The rotational motion expressed by was found to be most convenient to express the motion. It can be seen that the units in the armour layer rock much more often than visually observed. Settlement seems to be a continuous progress, with most units rocking intermittently. Highest impact velocities are seen to occur around the water line, and in the uprush phase of the waves. A maximum impact velocity for all tests of 0.34 m/s (model scale) was measured. A preliminary design expression for rocking impact velocities of single layer units (Xblocs) is given. The paper shows that novel measurement techniques like the Rocking Sensors and vision techniques can and should be used to quantify damage mechanisms to rubble mound single-layer armour, additional to counting the extracted number of intact units.
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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 | 0.000 | 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".