Data Sensing and Processing Tensioning System Based on the Internet of Things
Bibliographic record
Abstract
Tensioning is an important process for producing prestressed concrete beams and directly affects bridge performance and driving safety. Active sensing and management of tensioning process data can improve the efficiency of quality monitoring and level of prestressed concrete beams. To realize remote collection and quality monitoring of tensioning process data, a framework for data sensing and processing of tensioning system based on the Internet of Things (IoT) is proposed in this study. Firstly, we investigate the technical framework and techniques of the system and designs a work flow of sensing, transport, and application service layers. The architecture of the tensioning system is presented. Then we propose a data acquisition and preprocessing method for the sensing layer, put forwards the data-pushing mechanism of the transport layer, and designs the function and work flow of the application service layer. After that, .NET platform and Android Studio are used to implement the tensioning system based on Browser/Server (B/S) architecture and mobile terminals. Finally, the case results of the system in seven precast beam fields in the Hubei section of Zhengzhou–Wanzhou high-speed Railway are given, which show that the developed system realizes collection, active pushing, and remote monitoring of tensioning process data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".