Smart Bridges Will Solve Our Bridge Crisis
Bibliographic record
Abstract
Bridges are a large potential market for Structural Health monitoring (SHM), but SHM has had very little market penetration. Yet at the same time there exists a growing bridge crisis. At the current rate of investment, it will take until 2071 to make all the repairs that are currently necessary, and the additional deterioration over the next 50 years will become overwhelming. The nation needs a systematic performance-based program for bridge preservation, whereby existing deterioration is prioritized, and the focus is on rehabilitation and preventive maintenance. The approach the USA is taking to Bridge Asset Management is not working and needs to change if we are to improve and not degrade our transportation system. The industry needs to innovate to solve the problem resulting in increased productivity or in the case of bridges lower life cycle costs while maintaining full functionality. Structural Health monitoring (SHM) has been used for many years but has not gained widespread adoption due to it being a partial solution to the bridge asset life cycle management problem and uneconomical to implement. Industry consensus is that Generation 1 SHM alone will not solve the bridge crisis. What is needed is a Smart Bridge using a total solution provided by a digital enterprise bridge performance management platform based on an IoT/cloud architecture that includes: Structural and Operating performance measurement and monitoring, Real-time analytics and alarms, and fact-based decisions for improved bridge life cycle performance and economics. This paper will present the result of implementing a Smart Bridge strategy on a fleet of bridges by NYSDOT USA and Manitoba Canada. Their success with Smart Bridges has led to a comprehensive bridge lifecycle management strategy aimed at creating 150- year-old bridges through performance measurement and rehabilitation. This strategy is projected to save hundreds of millions of dollars over 20 years. Smart Bridges are crucial for modern smart cities, integrating digital technologies to optimize bridge performance, reduce congestion, enhance public safety, and support autonomous vehicles. They offer a transformative approach to bridge asset management, supplementing outdated visual inspection methods with advanced IoT technology and data analytics. Both NYSDOT and Manitoba Infrastructure exemplify best practices in innovative bridge asset management, demonstrating the significant benefits of Smart Bridge strategies. These initiatives provide a blueprint for other regions to follow, ensuring the sustainability and reliability of critical bridge infrastructures for future generations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".