Multibridge Inference Structural Health Monitoring (MISHM): A Drive‐By Crowdsensing Approach at the Network Level
Why this work is in the frame
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Bibliographic record
Abstract
As aging bridge infrastructure poses increasing safety risks, there is a critical need for reliable and scalable Structural Health Monitoring (SHM) systems. Traditional SHM methods, which rely on fixed sensor networks and assessments of individual bridges, face significant challenges in scalability, cost, and efficiency—particularly in complex urban environments. To address these limitations, this study introduces the Multibridge Inference SHM (MISHM) framework. MISHM leverages drive‐by monitoring and crowdsensing to observe multiple bridges simultaneously. It employs a feature‐based analysis using Mel‐frequency cepstral coefficients (MFCCs) and Kullback–Leibler (KL) Divergence to identify structural changes. Here, “inference” refers to drawing conclusions about the health of each individual bridge by comparing patterns and features gleaned from the entire network, rather than relying on isolated measurements. By making multiple comparisons across all monitored structures, MISHM enhances fault tolerance, reduces missed detections, and offers a scalable solution for smart city infrastructure monitoring. This framework represents a vital advancement in SHM systems, addressing the evolving needs of large‐scale urban infrastructure management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it