Dear Colleagues,

With the rise of next-generation sensing, learning, and analytics frameworks and platforms, we observe a significant step towards data-enabled automation in managing transportation infrastructure assets. The mechanical behavior of transportation infrastructure is subject to change over time, and digitized early warnings reflecting infrastructure conditions can dramatically increase the efficiency of stakeholders’ decisions without conventional labor-intensive efforts taking place. Concerning deterioration and failure, bridge structures are among the specific weak links of transportation networks; although they are not the sole degradable assets, and many other sources can trigger transportation service disruption.

This Special Issue is a broad attempt to bring state-of-the-art research on various aspects of bridge mechanics, from dynamic modeling and simulations to inverse dynamics incorporating diagnostics techniques such as structural health monitoring. The Special Issue is not only limited to the technical developments in modeling and monitoring frontiers but also covers the stakeholder-end post-diagnostics processes, i.e., consequence metrics, decision models, and potential maintenance actions conditioned to infrastructure diagnostics. Parallel advances in sensor technologies and hardware, finite element and surrogate modeling prospects, statistical learning approaches, hazard analysis and decision-making strategies are encouraged for representation in the Special Issue.

We hope this initiative will unify the existing research trends in bridge engineering for a complete understanding of data-enabled bridge risk mitigation and generate the essential knowledge for the 21st century’s digital transformation that can lead to more sustainable and resilient transportation infrastructure. Future recommender systems for bridge assets can be a vital part of this scheme, particularly addressed in this Special Issue. Topics included in this Special Issue include but are not limited to:

• Bridge health monitoring
• Bridge condition assessment/diagnostics
• Emerging sensing techniques/sensor technologies for bridge monitoring
• Bridge dynamics
• Finite element modeling and model updating
• Damage identification and classification
• Predictive systems for prognostics/remaining useful life
• Digital twins
• Machine learning applications for bridge infrastructure
• Hazard and risk analysis for bridges
• Performance assessment of bridges
• Bridge decision support systems
• Bridge maintenance, retrofit, and management
• Bridge networks

Yours sincerely,

Dr. Ekin Ozer
Guest Editor

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• bridge
• sensor
• dynamics
• finite element
• model
• monitoring
• reliability
• risk
• diagnostics
• prognostics
• machine learning
• twins
• damage
• maintenance
• decision