Dear Colleagues,

Civil structures and infrastructures are susceptible to damage caused by natural hazards and extreme events such as earthquakes, strong winds and flooding, as well as material degradation and increasing traffic actions. Data-driven structural health monitoring (SHM) techniques have emerged as valuable tools for assessing the current structural state of conservation. Additionally, model-based approaches can provide support, both in identifying damage and in planning repair operations, as well as in properly managing the limited funding for maintenance interventions, thereby ensuring the safety and functionality of structures and infrastructures.

Traditionally, SHM systems involve the installation of dynamic and static sensors on-site, along with data acquisition systems to record various types of information over time. However, these data can be highly uncertain due to the complexity of structural systems and the influence of external factors like environmental noise (i.e., temperature, relative humidity, etc.).

In this context, SHM is a growing field, although there is still the need for research efforts to create probabilistic frameworks using automated numerical tools that can handle various sources of uncertainties. Additionally, it is of paramount importance to address the topic of damage identification, which also includes detection, localization, classification, assessment, and, above all, the prediction of its evolution.

This Special Issue aims to highlight and discuss new developments, inviting high-quality contributions that focus on the investigation of the current state-of-the-art, recent advancements, practical applications, and future perspectives in SHM for structures and infrastructures. The contributions can cover the following topics:

• In-depth reviews and innovative contributions in probabilistic-based SHM techniques for structures and infrastructures.
• Recent developments in probabilistic-based techniques as decision-support tools for evaluating structural integrity.
• Recent advancements in SHM technologies.
• Novel methods for data fusion.
• The use of surrogate modeling for automated damage identification.
• The application of life-cycle cost analysis to reduce operational costs and risks associated with SHM.

Dr. Laura Ierimonti
Dr. Laura Gioiella
Dr. Michele Morici
Guest Editors

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• structural health monitoring
• damage detection
• data fusion
• probabilistic risk assessment
• decision-making
• life-cycle cost analysis
• surrogate modeling