  Most seismic risk assessment methods focus on estimating the damages to the
built environment and the consequent socioeconomic losses without fully taking
into account the social aspect of risk. Yet, human behaviour is a key element
in predicting the human impact of an earthquake, therefore, it is important to
include it in quantitative risk assessment studies. In this study, an
interdisciplinary approach simulating pedestrians' evacuation during
earthquakes at the city scale is developed using an agent-based model. The
model integrates the seismic hazard, the physical vulnerability as well as
individuals' behaviours and mobility. The simulator is applied to the case of
Beirut, Lebanon. Lebanon is at the heart of the Levant fault system that has
generated several Mw>7 earthquakes, the latest being in 1759. It is one of the
countries with the highest seismic risk in the Mediterranean region. This is
due to the high seismic vulnerability of the buildings due to the absence of
mandatory seismic regulation until 2012, the high level of urbanization, and
the lack of adequate spatial planning and risk prevention policies. Beirut as
the main residential, economic and institutional hub of Lebanon is densely
populated. To accommodate the growing need for urban development, constructions
have almost taken over all of the green areas of the city; squares and gardens
are disappearing to give place to skyscrapers. However, open spaces are safe
places to shelter, away from debris, and therefore play an essential role in
earthquake evacuation. Despite the massive urbanization, there are a few open
spaces but locked gates and other types of anthropogenic barriers often limit
their access. To simulate this complex context, pedestrians' evacuation
simulations are run in a highly realistic spatial environment implemented in
GAMA [1]. Previous data concerning soil and buildings in Beirut [2, 3] are
complemented by new geographic data extracted from high-resolution Pleiades
satellite images. The seismic loading is defined as a peak ground acceleration
of 0.3g, as stated in Lebanese seismic regulations. Building damages are
estimated using an artificial neural network trained to predict the mean damage
[4] based on the seismic loading as well as the soil and building vibrational
properties [5]. Moreover, the quantity and the footprint of the generated
debris around each building are also estimated and included in the model. We
simulate how topography, buildings, debris, and access to open spaces, affect
individuals' mobility. Two city configurations are implemented: 1. Open spaces
are accessible without any barriers; 2. Access to some open spaces is blocked.
The first simulation results show that while 52% of the population is able to
arrive to an open space within 5 minutes after an earthquake, this number is
reduced to 39% when one of the open spaces is locked. These results show that
the presence of accessible open spaces in a city and their proximity to the
residential buildings is a crucial factor for ensuring people's safety when an
earthquake occurs.
