Agent-based model on resilience-oriented rapid responses of road networks under seismic hazard

This paper explores a new pathway towards seismic resilience of Road Networks (RNs) under earthquake hazards, by leveraging post-shock rapid responses as the key to minimize the functionality losses of RNs, especially in the immediate aftermath of earthquakes. Accordingly, an agent-based modelling (ABM) framework is developed to enable the nuanced examination on resilience of earthquake-damaged RNs, when different system repair approaches are considered. In this framework, those different approaches are predicated on the damage level of individual bridges and on the system recovery timeline, i.e. the response to rehabilitation need is considered as a function of the time elapsed from the event. Each approach is represented by a different agent, whose behaviour is shaped by a set of pre-defined behavioural attributes, while the interplay among those agents is also accounted for, during the entirety of post-shock recovery campaigns. To demonstrate its applicability, the ABM framework is applied to a real-world RN across Luchon, France. As shown by the case-study, post-shock rapid responses are found to be a viable strategy to increase the recovery rate of RNs’ functionality in the immediate-, and mid-term aftermath of damaging earthquakes, and ultimately, to improve the seismic resilience thereof

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

Within moments following an earthquake event, observations collected from the affected area can be used to define a picture of expected losses and to provide emergency services with accurate information. A Bayesian Network framework could be used to update the prior loss estimates based on ground-motion prediction equations and fragility curves, considering various field observations (i.e., evidence). While very appealing in theory, Bayesian Networks pose many challenges when applied to real-world infrastructure systems, especially in terms of scalability. The present study explores the applicability of approximate Bayesian inference, based on Monte-Carlo Markov-Chain sampling algorithms, to a real-world network of roads and built areas where expected loss metrics pertain to the accessibility between damaged areas and hospitals in the region. Observations are gathered either from free-field stations (for updating the ground-motion field) or from structure-mounted stations (for the updating of the damage states of infrastructure components). It is found that the proposed Bayesian approach is able to process a system comprising hundreds of components with reasonable accuracy, time and computation cost. Emergency managers may readily use the updated loss distributions to make informed decisions

Sensitivity of High-Frequency Ground Motion to Kinematic Source Parameters

International audienceEmpirical ground motion prediction equations are calibrated from past earthquake seismic recordings. Although they are often used to predict Peak Ground Acceleration (PGA) and its variability, the use of these equations to predict near-fault PGA remains questionable due to the scarcity of near-fault recordings for large earthquakes (e.g. Mai Encyclopedia of complexity and systems science (pp. 4435–4474). New York: Springer. https://doi.org/10.1007/978-0-387-30440-3_263. 2009). The simulation of strong ground motion offers an attractive alternative for the assessment of near-fault seismic hazards, but the a priori choice of the source parameters used to describe the fault rupture process remains a complex issue. In order to better understand the effects of rupture parameters on surface ground motion and to capture the key source ingredients that impact ground motion variability, we simulated ground motions produced by various M7 strike-slip rupture earthquake scenarios on vertical faults. We computed ground motion up to 5 Hz using the far-field approximation as well as at the near-field stations located at 5 km, 25 km and 70 km from the fault (assuming a visco-elastic medium). The kinematic rupture parameters are modeled using a statistical rupture model generator as proposed by Song et al. Geophysical Journal International,196(3), 1770–1786 (2014). Our work demonstrates that PGA is mostly generated by abrupt changes in the rupture propagation (e.g. stopping phases at the fault boundaries or strong heterogeneities of rupture speed along the fault). We observed that PGA is mostly controlled by average rupture speed and average stress drop (in the far-field), and to a lesser extent by the standard deviation of the rupture speed. It is worth noting that for the set of stations in study, the correlation between source parameters and spatial correlation length does not affect average PGA and related variability significantly