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.\u00a0The 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:\u00a0Optimal sensor layout and automated damage feature extraction (including automated modal parameter identification);\u00a0Influence of environmental and operational variability on SHM reliability and compensation methods;\u00a0Data mining and data fusion approaches for civil SHM;\u00a0Damage feature selection and comparative assessment of damage sensitivity of different damage indexes;\u00a0Approaches for damage detection, location, extension, and classification from response measurements;\u00a0Techniques for robust solution of the inverse problem in model-based SHM techniques;\u00a0Artificial intelligence in civil SHM;\u00a0Comparative assessment of data-driven and model-based SHM approaches in the context of a given damage scenario;\u00a0Residual life prediction;\u00a0Role of SHM in decision making, including early warning, emergency management, and support to structural maintenance in service conditions.
\nKeywords: structural health monitoring;\u00a0influence of environmental factors;\u00a0damage features;\u00a0inverse problems;\u00a0artificial intelligence;\u00a0residual life.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"template":"","format":"standard","topic":[15,586,568,644,576,597,598,625,601,570,465,559,676,661,470,667,572,660,616,604,596,677,551,672],"country":[55],"nsj_language":[57],"class_list":["post-1336","call_for_papers","type-call_for_papers","status-publish","format-standard","hentry","topic-civil-engineering","topic-construction-systems-engineering","topic-construction-technology","topic-data-analysis-processes","topic-data-management","topic-data-sensing-and-analysis","topic-decision-support-systems","topic-dynamic-models","topic-earthquake-engineering","topic-geotechnical-engineering","topic-ict","topic-industrial-engineering","topic-infrastructures","topic-innovative-techniques","topic-internet-of-things","topic-methods-for-diagnostics","topic-monitoring","topic-nondestructive-tests","topic-performance-evaluation","topic-process-modeling-and-simulation","topic-risk-management","topic-structural-dynamics","topic-structural-engineering","topic-urban-infrastructures","country-switzerland","nsj_language-english"],"acf":[],"yoast_head":"\n