Smart Bridges Will Solve Our Bridge Crisis
Notice bibliographique
Résumé
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 They offer a transformative approach to bridge asset management, supplementing outdated visual inspection methods with advanced IoT technology and data analytics.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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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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