Real-Time Construction Monitoring with a Wireless Shape-Acceleration Array System
Bibliographic record
Abstract
The work presented in this paper constitutes a major step toward the establishment of a cost-effective autonomous monitoring technology for soil and soil-structure systems. The Shape-Acceleration Array (SAA) takes advantage of developments in Micro-Electro-Mechanical Systems (MEMS) technologies. This sensor array is capable of simultaneously measuring 3D ground deformations and 3D soil vibrations up to a depth of one hundred meters. The significance of the Shape-Acceleration Array (SAA) is its wireless data transmission and the accuracy of the deformation measurement. The SAA is capable of measuring in situ (field) 3D ground deformation every 0.305 meters and 3D soil vibration at 2.4 m intervals. The system accuracy of the SAA is ±1.5 mm per 30 m; an empirically derived specification from a large number of datasets. This sensor array can be installed vertically, in a method similar to that used for traditional inclinometer casing, or horizontally. Each sensor array is connected to a wireless sensor node to enable real-time monitoring as well as remote sensor configuration. This paper presents the evolving design and installation methodology of this new sensor array as well as comparative results between the vertical SAA system and traditional instrumentation from a bridge replacement site in upstate New York.
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 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 | 0.000 |
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 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".