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This paper explores the potential of a low-cost, advanced video-based technique for the assessment of structural damage to buildings caused by seismic loading. A low-cost, high-speed video camera was utilized for the motion magnification processing of footage of a two-story reinforced-concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e., modal parameters) and the structural deformations of the building in magnified videos. The results using the motion magnification procedure were compared for validation of the method of the damage assessment obtained through analyses of conventional accelerometric sensors and high-precision optical markers tracked using a passive 3D motion capture system. In addition, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometric recordings were also processed and analyzed using several stationary and nonstationary signal processing techniques with the aim of analyzing the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed procedure based on the analysis of magnified videos provided an accurate estimate of the main modal frequency and the damage location through the analysis of the modal shapes, which were confirmed using advanced analyses of the accelerometric data. Consequently, the main novelty of the study was the highlighting of a simple procedure with high potential for the extraction and analysis of modal parameters, with a special focus on the analysis of the modal shape's curvature, which provides accurate information on the location of the damage in a structure, while using a noncontact and low-cost method.

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In recent years, the ENEA has introduced a novel methodology based on motion magnification (MM) into the Italian cultural heritage protection and monitoring field. It consists of a digital video signal processing technique able to amplify enormously the tiny movements recorded in conventional videos, while preserving the general topology of the acquired frames. Though the idea of such a methodology is not new, it has recently been provided with an efficient algorithm that makes possible a viable and low-cost magnification. Applications are extremely varied in almost every field of science and technology; however, we are interested in its application to the safeguarding of architectural heritage, a sector of the utmost importance for Italy. As ancient buildings can be extremely sensitive to even minimally invasive instrumentation, most common monitoring sensors can be replaced by contactless tools and methods, such as video-based techniques like MM. It offers many advantages: easy to use, contactless devices, virtual sensors, reusability of the videos, practicality, intuitive graphical results, quantitative analyses capability and low costs. These characteristics are well suited to the monitoring of large ancient monuments; on the other hand, historical sites have peculiarities of their own, requiring careful approaches, proper tools and trained personnel. Moreover, outdoor applications of MM present quite notable difficulties from a practical point of view, e.g., the dimensions of the studied objects, uncontrolled environmental conditions, spurious vibrations, lighting change/instability, etc. Here we give a general idea of the potential of MM and related issues, using some relevant in-the-field case studies in Italian heritage protection.

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Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified.