In recent years, structural health monitoring (SHM) has received increasing interest from both research and professional engineering communities. This is due to the limitations related to the use of traditional methods based on visual inspection for a rapid and effective assessment of structures and infrastructures when compared with the great potential offered by newly developed automatic systems. Most of these kinds of systems allow the continuous estimation of structural modal properties that are strictly correlated to the mechanical characteristics of the monitored structure. These can change as a result of material deterioration and structural damage related to earthquake shaking. Furthermore, a suitable configuration of a dense sensor network in a real-time monitoring system can allow to detect and localize structural and non-structural damage by comparing the initial and a final state of the structure after a critical event, such as a relevant earthquake. In this paper, the modal curvature evaluation method, used for damage detection and localization on framed structures, considering the mode curvature variation due to strong earthquake shaking, is further developed. The modified approach is validated by numerical and experimental case studies. The extended procedure, named “Curvature Evolution Method” (CEM), reduces the required computing time and the uncertainties in the results. Furthermore, in this work, an empirical relationship between curvature variation and damage index has been defined for both bare and infilled frames.

The Multi-Parameter Wireless Sensing (MPwise) system is an innovative instrumental design that allows different sensor types to be combined with relatively high-performance computing and communications components. These units, which incorporate off-the-shelf components, can undertake complex information integration and processing tasks at the individual unit or node level (when used in a network), allowing the establishment of networks that are linked by advanced, robust and rapid communications routing and network topologies. The system (and its predecessors) was originally designed for earthquake risk mitigation, including earthquake early warning (EEW), rapid response actions, structural health monitoring, and site-effect characterization. For EEW, MPwise units are capable of on-site, decentralized, independent analysis of the recorded ground motion and based on this, may issue an appropriate warning, either by the unit itself or transmitted throughout a network by dedicated alarming procedures. The multi-sensor capabilities of the system allow it to be instrumented with standard strong- and weak-motion sensors, broadband sensors, MEMS (namely accelerometers), cameras, temperature and humidity sensors, and GNSS receivers. In this work, the MPwise hardware, software and communications schema are described, as well as an overview of its possible applications. While focusing on earthquake risk mitigation actions, the aim in the future is to expand its capabilities towards a more multi-hazard and risk mitigation role. Overall, MPwise offers considerable flexibility and has great potential in contributing to natural hazard risk mitigation.

Detailed knowledge of the 3D basin structure underlying urban areas is of major importance for improving the assessment of seismic hazard and risk. However, mapping the major features of the shallow geological layers becomes expensive where large areas need to be covered. In this study, we propose an innovative tool, based mainly on single station noise recordings and the horizontal-to-vertical spectral ratio (H/V), to identify and locate the depth of major impedance contrasts. The method is based on an identification of so-called fingerprints of the major impedance discontinuities and their migration to depth by means of an analytical procedure. The method is applied to seismic noise recordings collected in the city of Almaty (Kazakhstan). The estimated impedance contrasts vs. depth profiles are interpolated in order to derive a three-dimensional (3D) model, which after calibration with some available boreholes data allows the major tectonic features in the subsurface to be identified.

Seismic monitoring in areas where induced earthquakes could occur is a challenging topic for seismologists due to the generally very low signal to noise ratio. Therefore, the seismological community is devoting several efforts to the development of high-quality networks around the areas where fluid injection and storage and geothermal activities take place, also following the national induced seismicity monitoring guidelines. The use of advanced data mining strategies, such as template matching filters, auto-similarity search, and deep-learning approaches, has recently further fostered such monitoring, enhancing the seismic catalogs and lowering the magnitude of completeness of these areas. In this framework, we carried out an experiment where a small-aperture seismic array was installed within the dense seismic network used for monitoring the gas reservoir of Collalto, in North Italy. The continuous velocimetric data, acquired for 25 days, were analysed through the application of the optimized auto-similarity search technique FAST. The array was conceived as a cost-effective network, aimed at integrating, right above the gas storage site, the permanent high-resolution Collalto Seismic Network. The analysis allowed to detect micro-events down to magnitude Ml = −0.4 within a distance of ~15 km from the array. Our results confirmed that the system based on the array installation and the FAST data analysis might contribute to lowering the magnitude of completeness around the site of about 0.7 units.