Resilience assessment of seismic-prone urban areas

The concussion of globalization, urbanization and climate change make crisis to be normality for modern cities, begin always threatened by the increasing occurrence rate of natural disasters. To date, not enough time goes by without a disruption affects a city somewhere in the world. While these catastrophic events cannot be forecast, local communities can learn from them. They can strive to make cities resilient. With this, the primary goal of the present study is to quantify urban resilience to seismic events. Particularly, a human-centric perspective is adopted. Social actors represent, in fact, the main constituent of urban systems, being interconnected to the physical structure and linking humanitarian and progress efforts. As a consequence, to effectively assess urban resilience against earthquakes, cities are understood as complex systems, focusing on each single constituent but also on their underlying topology. An experimental framework is proposed, which model cities as graphs. The physical and the social network are separately modelled and then overlaid within the geographical layer. As a result, a hybrid social-physical network (HSPN) is shaped, accounting for all urban fundamentals. Synthetic city models with diverse topological configurations and size are modelled, and scenario analysis are performed to evaluate urban efficiency, according to the complex network theory. Furthermore a real case analysis is also studied for the city of Sarno, Italy. Locally-driven recovery strategies are designed, to simulate the restoration process for each city model, after the event occurrence. The city efficiency is evaluated before and soon after the event occurrence, and in each stage of the simulated recovery strategy. Furthermore the systemic damage is assessed, as a proxy of efficiency, as the loss of functionality over the HSPN. Finally, urban resilience is quantified, to account for the diverse response of urban environments. An alternative resilience metric is also proposed, being dependant on the initial state of damage. Results from synthetic HSPNs show essential differences in the evaluation of city resilience, according to the city size. In this case, disaster resilience is observed to be a scaling function of the city size (Bettencourt et al. 2007, 7301–7306). The robustness of the two alternative resilience metrics with extent of the seismic damage is observed on the real case study. In this case, results show greater consistency of the damage-dependent resilience metric with the event severity.