Fragility curves are essential tools to quantitatively assess the physical vulnerability of structures and infrastructures at risk for a given seismic hazard. They describe the probability of exceeding a given performance level under earthquake excitation, and are usually defined by a lognormal probability distribution function. Although debris from damaged buildings adjacent to road edges is the main cause of urban mobility disruption, studies on the fragility curves development for infrastructures subject to seismic actions focus on geotechnical effects, and do not analyze this type of road blockage. The article proposes an analytical procedure to construct fragility curves for urban road networks. It is based on the construction of debris graphs and the use of an appropriate fitting technique. For a given seismic intensity measure level, the developed fragility curves express the probability that the road is open or closed to the transit of emergency vehicles after debris fall. Therefore, the performance level is defined in terms of the width of the road pavement that remains free after the debris fall, or the width of the debris heap on the road pavement. Finally, the proposed framework is tested with real data of the main street in Amatrice, and the results are presented and discussed.

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.

The peak roof drift ratio is one of the most important engineering parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a machine learning approach (Gaussian process regression model) on a dataset of approximately 11,800 records from 34 monitored buildings in Japan. Four predictors for ground motion and three predictors for building vulnerability are used in the machine-learning modelling. The residual analysis shows a reduction of 50% compared to the state of the art. The Gaussian process regression model is applied in a second analysis on an original dataset of approximately 4,500 records for 127 monitored buildings in Italy. A satisfactory comparison emerges by comparing the drift ratio prediction map with the observed damage pattern produced by satellite imagery for a test site in central Italy after the 2009 earthquake. The drift ratio map plays an important role in the simulation of an earthquake scenario at regional scale, which is needed by Civil Protection for emergency planning and management activities.

The effects induced by the choice of numerical base conditions for evaluating local seismic response are investigated in this technical note, aiming to provide guidelines for professional applications. A numerical modelling of the seismic site response is presented, assuming a one-dimensional scheme. At first, with reference to the case of a homogeneous soil layer overlying a half-space, two different types of numerical base conditions, named rigid and elastic, were adopted to analyse the seismic site response. Then, geological setting, physical and mechanical properties were selected from Italian case studies. In detail, the following stratigraphic successions were considered: shallow layer 1 (shear wave velocity, V_S, equal to 400 m/s), layer 2 (V_S equal to 600 m/s) and layer 3 (V_S equal to 800 m/s). In addition, real signals were retrieved from the web site of the Italian accelerometric strong motion network. Rigid and elastic base conditions were adopted to estimate the ground motion modifications of the reference signals. The results are presented in terms of amplification factors (i.e., ratio of integral quantities referred to free-field and reference response spectra) and are compared between the adopted numerical models.