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A large part of Europe is exposed to medium/high seismic risk. Throughout the past decades serious structural damage and collapse have occurred in different countries. Examples of structures at risk are existing infrastructure and public buildings. Efficient seismic protection is especially required in these structures. In fact, an earthquake can lead to a high number of injury or death since these structures are often crowded. On the other hand, strategic structures have to be fully operational to manage the aftershock emergencies. This article deals with some results of a research focused on the development of optimized structural health monitoring (SHM) technologies and data processing techniques for critical structures in seismically prone areas. Specific solutions are proposed to take advantage of seismic early warning systems (SEWSs), which are becoming very popular and effective worldwide. The most relevant aspects of seismic early warning (SEW) and SHM systems are herein reviewed and the main issues related to their integration are discussed in order to properly design the final system. Attention is mainly focused on dynamic behavior in operational conditions and on earthquake effects. Hardware and software solutions adopted for the characterization and monitoring of the dynamic response of a sample building are illustrated pointing out the capability of the same architecture to host data and information provided by SEW applications. Finally, datasets in operational conditions are used to evaluate the fundamental modal parameters of the structure by output-only techniques, whose potentialities and limitations in the presence of weakly and heavily nonstationary signals have been also assessed. In particular, they have been applied, respectively, to the records collected during crowded football matches hosted at the stadium located nearby the sample building and during the recent L’Aquila earthquake mainshock

Structural aging, degradation phenomena, and damage due to hazardous events are common causes of failure in civil structures and infrastructures. The increasing need of extending the structure lifespan for sustainability and economic reasons motivated the rapid development of remote, fully automated structural health monitoring systems. Different approaches have been developed for damage detection based on the incoming data. Modal-based damage detection is probably one of the most popular procedures for structural health monitoring of civil structures, also thanks to the development of robust automated operational modal analysis algorithms in the last decade. However, the sensitivity of modal parameter estimates and the associated damage features to environmental and operational factors represents a significant drawback to the extensive application of this technology. Thus, effective damage detection cannot skip the preliminary compensation of the effect of those variables on modal properties. Different approaches to compensate the environmental influence on modal property estimates are reported in the literature. In this article, the use of Second-Order Blind Identification is proposed. It is applied to a number of case studies in order to validate its effectiveness in the presence of one or more environmental or operational variables. Results demonstrate that it can model the variability of natural frequency estimates in operational conditions and, above all, it can give a fundamental insight in determining the causes of such variability.