Timely damage detection is critical to ensure safe operation of civil infrastructures and informed management and maintenance. Two dominant competing philosophies for civil Structural Health Monitoring (SHM) have emerged in the last decades: data driven methods, and model-based approaches. However, several aspects are still worth of investigation, such as the selection and automatic extraction of damage sensitive features, the compensation of environmental and operational influence on damage sensitive features, the appropriate setting of models and thresholds, the role of system identification and model updating for damage assessment, and the support to decision making.

The goal of this Special Issue is to discuss the latest achievements in the field of SHM of civil infrastructures, encouraging multidisciplinary contributions. Potential topics for submissions include but are not limited to:

·         Automated damage sensitive feature extraction (including automated Operational Modal Analysis);

·         Influence of environmental and operational variability on damage sensitive features;

·         Machine Learning for vibration-based SHM;

·         Optimal sensor layout for vibration-based SHM;

·         Approaches for damage detection, location, extension and classification from response measurements;

·         Model updating techniques and their application in the context of vibration-based SHM;

·         Comparative assessment of data driven and model-based SHM approaches;

·         Role of SHM in decision making.