The seismic risk assessment is typically conducted through the numerical modelling of the seismic site response to predict the shaking at the ground surface of a specific site. The estimation of the seismic site response may be conducted by adopting one-dimensional, two- or three-dimensional schemes, as a function of the site conditions, and implementing linear visco-elastic or nonlinear elasto-plastic constitutive assumptions for the description of the cyclic soil behaviour. The predictive capacity of these numerical schemes depends on several factors, such as the extension of the domain, number of elements, boundary conditions, soil constitutive model and features of the input motions. Accurate site response predictions imply the adoption of detailed and sophisticated numerical models, which may be extremely time-consuming and require huge computational resources. Very often, the complexity of such numerical models is reduced in favour of simplified, faster but also less accurate schemes.

Within this framework, the Italian program of the National Research Centre in High-Performance Computing (HPC), Big Data and Quantum Computing (ICSC) “Environment and Natural Disasters (Spoke 5)” aims at developing advanced numerical tools for the real-time simulation of natural disaster-inducing phenomena, such as seismic wave propagation processes, with the primary objective of reducing the associated risks. In this context, the work illustrates the results of a preliminary numerical investigation aimed at assessing the performance of nonlinear 2D finite element (FE) analyses for the assessment of the seismic response of a natural slope. The FE slope model has been inspired by the western slope of Chieuti (Foggia), a small village located in the south of Italy, very well characterised from a geotechnical point of view. The simulations have been conducted with the FE code OpenSees, taking advantage of the National Research Centre HPC resources to improve their efficiency and using an in-house python pre-processor to compile the executable files. The cyclic response of the slope soils has been described by the Pressure Independent Multi Yield (PIMY) model, a nonlinear elasto-plastic constitutive model accounting for both isotropic and kinematic hardening. The slope model developed in OpenSees adopts 9_8_QuadUP finite elements, implementing the u-p formulation for the solid-fluid interaction during fully-coupled dynamic simulations. As benchmark, the same slope has been modelled in the FE code PLAXIS 2D. In this latter case, the nonlinear elasto-plastic constitutive model HSsmall has been adopted to describe the cyclic soil response. Both constitutive models have been calibrated based on the laboratory and in-situ tests available for the site of reference.

The performance of the OpenSees and PLAXIS dynamic analyses has been assessed in terms of displacement field and acceleration time histories predicted at the ground surface. The comparison shows a good agreement between the numerical predictions obtained with the two codes, with a clear advantage of reducing the computational cost when using OpenSees. The results prove how the HPC resources may enable cost-effective analyses of complex geotechnical problems, demonstrating the potential of advanced computational techniques to improve the seismic resilience of critical infrastructure.

Geotechnical monitoring represents a fundamental pillar in the strategy for mitigating seismic risk in road infrastructures, especially within a national context characterized by high seismicity and hydrogeological vulnerability. This contribution provides a critical overview of the technologies and methodologies employed in the geotechnical monitoring of infrastructure works. The study thoroughly examines both traditional and innovative measurement instruments, integrated monitoring systems, and automated data acquisition networks. Attention is also given to the role of satellite data and its integration with predictive models for assessing the stability of slopes and engineering structures, which are critical components of road networks in seismic areas. The analysis highlights how continuous monitoring, supported by advanced geotechnical analyses and early warning systems, can significantly reduce the risks associated with seismic events and allow for more efficient management of emergencies and intervention priorities. The synthesis of the analyzed approaches contributes to the development of operational guidelines for the implementation of effective monitoring systems, supporting resilience and sustainability policies for transportation infrastructures.

The basic purpose of site response analysis is to evaluate possible peak and spectral accelerations on the ground surface to estimate probable earthquake damage for the existing building stock and for the design of new structures. The basic issues in the site response analysis are the uncertainties in source characteristics, soil profile, soil properties, and site response analysis procedure. In addition, characteristics of the building inventories would introduce critical uncertainties associated with these analyses. Recent advances, with growing computational capacity, emphasize probabilistic frameworks to capture these uncertainties. The probability distribution of the related peak and spectral accelerations on the ground surface may be determined considering all possible input acceleration time histories, site profiles, and dynamic soil properties. One option to account for the variability in earthquake source and path effects may be to consider using large number of acceleration records compatible with the site-dependent earthquake hazard partially based on hazard deaggregation for the investigated site. Likewise, stochastic soil profiles generated via Monte Carlo simulations can be used to account for the site condition variability. A seismic microzonation methodology is proposed based on the probabilistic assessment of these factors involved in site response analyses. The second important issue is the selection of microzonation parameters. The selection of microzonation parameters such as Cumulative Absolute Velocity (CAV) and Housner Intensity (HI) is emphasized for their stronger empirical correlation with structural damage. The main approach is to develop a microzonation procedure for ground shaking intensity accounting for variability in ground motion and soil response. The third issue is the reliability and correctness of the site response analysis procedure. The adopted methodology advances traditional site response analysis by integrating frequency- and stress-dependent soil behavior models to achieve a more accurate numerical model and proposing the use of 3D site response analysis to reflect the multi-directional nature of seismic excitations. It also highlights the need for representative time histories with known exceedance probabilities. Even though the selected representative acceleration time histories may be scaled with respect to probabilistic acceleration spectrum or peak ground acceleration obtained based on probabilistic site response analysis; the probability of the selected acceleration time histories are not known. The only possible option is to estimate probabilistic acceleration time histories with predetermined exceedance probabilities to enhance fully probabilistic site response analysis. The proposed methodology is demonstrated through case studies to underline the importance of fully probabilistic site response analysis in seismic microzonation, aiming to improve the reliability of ground motion predictions and support informed decision-making in earthquake engineering.

The assessment of the Local Seismic Response (LSR) to the expected seismic motion in a territory is the subject of seismic microzonation studies. For the estimation of the seismic action, it is necessary to define some fundamental elements: the seismic input representative of the seismic action of the seismic bedrock and an adequate geotechnical model.

Generally, at least seven accelerograms compatible with the site's target response spectrum have to be identify. Typically, such accelerograms are chosen from available databases, in order to provide the best approximation of the target spectrum, based on an appropriate range of magnitude, epicentral distance, and epsilon parameter (SMEE), i.e. the number of standard deviations by which the observed logarithmic spectral acceleration differs from the mean logarithmic spectral acceleration of a ground-motion prediction by the attenuation equation.

These choice is made irrespective to all the time-dependent features characterizing the real signals of the area subject to microzonation (e. g. duration, evolution of amplitude and energy frequency content, total energy and its distribution over time, strong motion duration, etc.).

In a recent paper (Colajanni, Pagnotta, Testa, 2020) four methods for generating fully non-stationary artificial accelerograms on the basis of a target spectrum and a set of accelerograms registered in the neighborhood of the construction site, with provide a mean response spectrum that can be different from the target one. Among these, two of them, namely (Cacciola, 2010 (CA), and Rofooei, Mobarake & Ahmadi, 2001(RMA)) have proven to be particularly efficient in reproducing the characteristics of the events expected at the site. More precisely, the CA method provides reliable results, but they are highly influenced by the selection criterion of the group of real records through which the non-stationary counterpart is defined, the RMA is able to clearly define the non-stationary characteristics of a site, but gives controversial results, due to the absence of a spectrum-compatible formulation.

In this paper, the set of accelerograms obtained with the two generating methods are used as input accelerograms for a microzonation procedure, obtained from one-dimensional linear equivalent method site response analyses implemented in AlgoShake2D software (Algoritmiqa 2024), and compared with results obtained by microzonation performed through the natural accelerogram selection procedure currently used in the professional field mentioned above (SMEE) , and with the results obtained assuming as a signal to the bedrock a set of accelerograms actually recorded at the site, which constitute the benchmark in analogy to the procedure used in (Colajanni Pagnotta, Testa, 2020).

Urban seismic resilience is shaped not only by the structural integrity of buildings but also by the socio-economic vulnerabilities of the communities they shelter. In this study, we present a novel compiled dataset that bridges these two domains by integrating socio-economic indicators with engineering-based seismic resilience metrics for neighborhoods in Istanbul, a megacity with high seismic risk and socio-spatial inequality. Our hypothesis is that socio-economic disadvantage significantly influences whether people reside in earthquake-safe structures. To test this, we compiled and merged multiple publicly available datasets released by Istanbul Metropolitan Municipality, including the number of households receiving social aid, socio-economic status (SES) scores, and structural vulnerability parameters derived from seismic risk assessments. The dataset allows for a nuanced, data-driven understanding of post-earthquake recovery capacity across Istanbul’s neighborhoods. Initial findings reveal that areas with lower SES scores correlate with higher expected recovery time and cost, suggesting a compounded vulnerability. Beyond its research value, the dataset provides practical utility for municipalities, civil protection units, and policy-makers aiming to prioritize interventions. It enables targeted resilience enhancement strategies and facilitates simulations of neighborhood-level risk under various seismic scenarios.