Timely detection of damage is critical to ensuring the safe operation of bridges, wind turbines, and civil infrastructures more generally, allowing early warnings to be issued and to avoid significant life, economic, and secondary losses. Moreover, monitoring can provide relevant information for structural management and maintenance. While two dominant competing philosophies for civil Structural Health Monitoring (SHM) have emerged in the last decades (data driven vs. model-based approaches), several aspects are still worthy of investigation, including the selection of effective damage features and their automatic extraction from response measurements as well as sensitivity to environmental and operational factors, the appropriate setting of statistical models and thresholds in data-driven approaches, the role of system identification and model updating for damage assessment, the optimization techniques to use for a reliable solution of the inverse problem, the prediction of the remaining useful life of structures, and the support to decision making. The goal of this Special Issue is to discuss the latest achievements in the field of data processing procedures for SHM of civil infrastructures, and multidisciplinary contributions are especially encouraged. Potential topics for submissions include but are not limited to: Optimal sensor layout and automated damage feature extraction (including automated modal parameter identification); Influence of environmental and operational variability on SHM reliability and compensation methods; Data mining and data fusion approaches for civil SHM; Damage feature selection and comparative assessment of damage sensitivity of different damage indexes; Approaches for damage detection, location, extension, and classification from response measurements; Techniques for robust solution of the inverse problem in model-based SHM techniques; Artificial intelligence in civil SHM; Comparative assessment of data-driven and model-based SHM approaches in the context of a given damage scenario; Residual life prediction; Role of SHM in decision making, including early warning, emergency management, and support to structural maintenance in service conditions.
Keywords: structural health monitoring; influence of environmental factors; damage features; inverse problems; artificial intelligence; residual life.