Unstable Slope Monitoring with a Wireless Shape-Acceleration Array System
Bibliographic record
Abstract
The evaluation, health monitoring and response prediction of soil and soil-structure systems during construction and due to extreme hazard conditions are on the verge of a significant paradigm shift. New and less expensive sensing technologies have enabled the development of innovative instrumentation and advanced interactive modeling tools. These tools, combined with recent advances in information technology including wireless sensor networking, data mining, visualization and system identification, promise significant improvements in real time monitoring during construction, sensor-assisted design and early warning of impending failure. This paper presents the newly developed Wireless Shape-Acceleration Array (WSAA) sensor that measures multi-dimensional acceleration and deformation profiles and constitutes a major step toward autonomous monitoring technology for soil and soil-structure systems. The WSAA sensor employs micromachined electromechanical sensors (MEMS), which have enabled gravity-based shape calculation along a sensorized substrate. WSAA uses MEMS accelerometers in a pre-calibrated, geometrically constrained array to provide long-term stability. This sensor array is capable of measuring 2D soil acceleration and 3D permanent ground deformations to a depth of one hundred meters. Each sensor array is connected to a wireless earth station to enable real time monitoring of a wide range of soil and soil-structure systems as well as remote sensor configuration. This paper presents the evolving design of this new sensor array as well a description of and preliminary data from an instrumented unstable slope in California.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".