# Measuring the Seismic Resilience of Housing Systems in Italy
###### Abstract
It aims to enable fundamental resilience assessment algorithms considering the natural hazards to which the community is exposed, the physical infrastructure within the community, and the population demographies. Then, [PERSON] and [PERSON] [19] applied their multi-objective optimization model to the Centerville Virtual Community. By starting with a group of residential and commercial buildings and assigning them a given budget, this optimization model is able to analyze the trade-offs between direct economic loss and the goal of minimizing immediate population dislocation. An IdealCity model is presented by [PERSON] et al. [20] to simulate and study the disaster resilience of communities at the urban level for post-earthquake scenarios. The model is based on the PEOPLES resilience framework and considers the mutual interaction between different model layers (i.e., built environment, road network, generated debris, and agents), the effect of debris on evacuees' walking speed, as well as the simulation of the emergency response network (ambulances, hospitals, shelters). The building stock, population and its distribution in the IdealCity are representative of a typical Italian building portfolio, while occupancy and physical characteristics of structures and infrastructures (i.e., road transportation network, building archetype, year of construction, height classifications) reflect the characteristic of the town of Turin. Working with the IdealCity, an integrated platform was developed by [PERSON] et al. [21] to evaluate the seismic resilience of communities. The platform analyses different layers (i.e., road transportation networks, buildings, water distribution networks, power grid, and socio-technical networks) using specific models to simulate their interdependency. However, in the current state of the platform, the recovery of damaged structures and infrastructure cannot be considered. [PERSON] et al. [22] developed a stochastic formulation that is able to measure the resilience of engineering systems by estimating the impact of recovery activities and possible recovery interruptions on the damage state of these systems. To this aim, a reliability-based definition of damage levels was also proposed. The formulation was applied to investigate the resilience of a reinforced concrete (RC) bridge retrofitted with fiber-reinforced polymer. In the work of [PERSON] et al. [23], a new framework for the seismic resilience evaluation of single buildings is proposed to build resilient buildings. It was applied to a ten-story RC moment-resisting frame structure located in Shanghai. Currently, some elements now seem clear. Resilience models are conceptually very similar. However, they must be adapted to the context under consideration [24; 25]. The data and assessment models must also be specific to the community under consideration. They strongly affect evaluations, particularly in assessing initial conditions, defining recovery time and recovery functions, down time, and so on. As a result, procedures can vary widely in practical applications, and specific studies of community subparts (facilities, residential buildings, etc.) are needed (e.g., [26; 27]). While rigorous mathematical frameworks for resilience analysis are being developed, applications of these frameworks to real case studies focus on a local scale (neighborhood, district, city), or even single structures. The first large-scale resilience assessments have only recently been defined by mapping Urban Limit Conditions (ULCs) in southern Italy according to the seismic robustness of housing systems and specific ULC thresholds [28]. ULCs were formulated for the first time by several authors [29; 30; 31] and correspond to different urban functionality loss levels for earthquakes of increasing intensity [32]. Then, in the study of [PERSON] et al. [33], ULC thresholds were quantitatively defined for housing, education, economy, healthcare, governance and social system.