Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles
Bibliographic record
Abstract
Condition monitoring of bridge structures as the lifelines of smart cities is high of importance. While indirect vehicle scanning techniques have shown promising results and have less cost compared to mounting fixed sensors on the bridge, they have limitations in predicting response under traffic flow since the crossing time of one vehicle is very short. This paper presents a novel crowsensing-based framework for predicting bridge acceleration responses and identifying the modal characteristics. This method utilizes smartphone data from a diverse population of sensing vehicles as they traverse the bridge to predict bridge acceleration response at various virtual sensing locations. Subsequently, the predicted acceleration is used to identify the mode shapes and natural frequencies of the bridge. The principal innovation in this practical and cost-effective monitoring solution is the utilization of a randomly selected set of sensing vehicles at each timestamp. These selections may differ from one timestamp to the next, reflecting the real-world conditions where certain vehicles may intermittently lose or disconnect their internet connectivity. By consistently updating the set of sensing agents while other vehicles cross the bridge, the proposed framework overcomes the data length limitations of conventional vehicle-based methods by leveraging multiple vehicles and continuous data collection. Comprehensive numerical studies are conducted to evaluate the performance of the method. In the numerical investigations, a three-span bridge is subjected to the continuous passing of a large number of half-car vehicle models with random speeds and initial locations. Vehicle-bridge interaction is considered in the analysis. Utilizing a single randomly selected sensing agent at each timestamp, the results demonstrate the effectiveness of the framework in predicting bridge acceleration response with a relative error of less than 5%. Additionally, the method achieves an accuracy level of 95% in identifying the bridge's initial three mode shapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".