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Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by the local stratigraphic and/or topographic setting and buried morphologies (e.g., irregular sub-interface between soft and stiff soils) that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near-real-time prediction of ground motion variation over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16 000 accelerometric data points and about 46 000 geological and geophysical data points was retrieved from Italian and European databases. The intensity measures of interest were estimated based on nine input proxies. The adopted machine learning regression model (i.e., Gaussian process regression) allows for improving both the precision and the accuracy in the estimation of the intensity measures with respect to the available near-real-time prediction methods (i.e., ground motion prediction equation and ShakeMaps). In addition, maps with a 50 m × 50 m resolution were generated, providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed subsoil models. [ABSTRACT FROM AUTHOR]

Ground motion modification over large areas is generally evaluated by focusing on source effects disregarding local lithostratigraphic site conditions. Hence, amplification maps of peak ground acceleration and peak ground velocity are proposed to improve the forecast of ground motion on a national scale. Topological information about litho-type successions and soil mechanical behaviour were retrieved from the Italian database of seismic microzonation and more than 30 million of seismic site response analyses were performed to quantify the amplification factors (i.e. the ratio between expected ground motion at the site of interest and that at the outcropping engineering bedrock). The maximum value of the amplified peak ground acceleration on the Italian territory results in about twice as much as the value expected at the outcropping of the engineering bedrock. Finally, damage scenario maps based on the amplified ground motion could be produced as a supporting tool for urban planning and emergency system management. [ABSTRACT FROM AUTHOR]

Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30-40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects. [ABSTRACT FROM AUTHOR]

In the present work, the extension of the Seismic Model from Ambient Vibrations (SMAV) to historical masonry buildings is presented. The probabilistic simplified model, which has already been validated for ordinary building, is able to predict their seismic behaviour on the basis of its experimental modal parameters, taking into account the dependence between the resonance frequencies and the vibration amplitude, through a linear-equivalent analysis. The SMAV model is based on the definition of a Structural Serviceability Index (IOPS), expressing the probability of the building to preserve its structural functionality after an earthquake occurrence. As the dynamic parameters are extracted from the vibrations which occur during the normal operating conditions, this methodology is particularly in accordance with the non-invasiveness requirements of monumental buildings, both in terms of service maintaining and structural interventions. The different phases provided for by the methodology and the feasibility of their application to real cases are illustrated with reference to the town hall of Sansepolcro, "Palazzo delle Laudi", an Italian renaissance building of XIII century. [ABSTRACT FROM AUTHOR]