Description:

Environmental and Operational Variability (EOV) has been identified as one of the main challenges to achieve practical SHM strategies, and so has received a lot of attention by the SHM community in the last years. While this field may seem in a mature state, the issue of damage detectability in the aftermath of EOV is still largely unexplored. If your research deals with methods either dealing with EOV and/or damage detectability, we would be pleased to receive your contributions and join the discussion. Papers related to machine learning methods for compensation of EOV, feature normalization techniques, hybrid techniques dealing with explicit and implicit methods for compensation and studies considering the issue of damage detectability are especially welcome.

Description:

In response to increasing concerns about the impact of climate change, the development of environmentally friendly energy technologies has intensified. Wind energy is rapidly transforming into a sustainable, resource-efficient and cost-effective global technology. Wind turbine structural health monitoring (SHM) detects, locates and characterizes deterioration to allow maintenance activities to be completed on time. SHM has been widely used in a variety of engineering fields because of its capacity to adapt to unfavorable structural changes, improve structural reliability, and manage the life cycle of structures. SHM has the potential to become a wind energy harvester in the near future, especially for offshore wind farms. They are huge constructions in isolated locations that are vulnerable to intense environmental conditions caused by wind, waves, and currents. The structure is always subject to excitations, which is a distinguishing feature of the wind turbine environment. Therefore, the proposed SHM approaches must be able to deal with such environmental excitations. This Special Session encourages submissions on wind turbine SHM. In particular, submitted papers should clearly demonstrate original contributions and creative applications.

Description:

Advances in mobile sensing technologies combined with identification methods encompassing vehicle-bridge interaction have led to a new paradigm in structural health monitoring (SHM), namely, drive-by SHM. This session will address drive-by monitoring of transportation infrastructure assets. There has been rapid progress over the past two decades, extracting damage-sensitive features from data measured in vehicles to monitor bridges, railway tracks and road pavements. Despite these advances, numerous challenges remain that limit the effectiveness of drive-by techniques. Some of these are road profile and its variability in three dimensions, low-grade vehicle positional accuracy and inadequate knowledge of vehicle properties. Some exciting new concepts are emerging such as self-calibration potential of vehicle fleets, use of physics-informed machine learning and reduced-order models for rapid detection. This special session will contribute to those advances to make drive-by monitoring more accurate, sensitive, robust, and actionable.

Description:

Structural health monitoring (SHM) involves data collection, transmission, processing, and interpretation aimed to support decisions for integrity management. SHM information plays a key role in the life-cycle integrity-management of structures, offering the possibility to reduce the uncertainty of the structural condition and update predictive models of future structural performance. Decisions about the optimal actions, needed to keep the structure at the desired level of functionality, can be greatly improved thanks to the reduction in the uncertainty that affects reliability assessment. Despite the importance of SHM in decision-making, the connection between the information provided by monitoring and the data interpretation for the decision analysis needs to be better developed. One of the main reasons lies in the cross-sectoral and interdisciplinary nature of the topic that involves experts in the two connected, yet distinct, domains of SHM and decision analysis. The aim of this Special Session is to highlight the importance of this connection, to share knowledge and ideas, and foster future collaborations as well as developments on this topic.