SHM of Tahya Masr Cable-Stayed Bridge Using Resensys Wireless Sensors
Bibliographic record
Abstract
Structural Health Monitoring (SHM) is now a fundamental idea in our daily lives. It is related to many disciplines, including Civil Engineering, Aerospace Engineering, Mechanical Engineering, and Marine Engineering, among others. It provides a diagnosis of the structure's state at every moment of its residual life, improves understanding of structural behavior, and detects any change that occurs to any component of it or for the entire structure via some devices (Sensors) that may be wired or wireless, incorporating micro and nanotechnology in their components putting on it or on the part under study. These sensors are data collectors that send data to a laptop or computer for processing via a communication system. These data assist decision-makers in determining the structure's residual life and whether it requires maintenance or rebuilding. The wireless SHM system of a Tahya Masr cable-stayed bridge is developed in this study using Resensys SenSpotTMSensors. The following approaches are suggested to achieve the goal. Wireless sensors were used to measure strain and monitor vibration, strain, inclination, tilt, temperature, and humidity. These measurements help to develop a reference data set that can be used to monitor and detect changes in structural behavior that indicate damage. This study successfully assessed the viability of Resensys SenSpotTMwireless, and it provided a brief overview of vibration- and impedance-based SHM techniques appropriate for the cable-stayed bridge's pylon system. Finally, the Tahya Masr cable-stayed bridge in Egypt served as a successful test site for the smart sensor's applicability.
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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 | 0.000 | 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) | 0.001 | 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".