Browsing by Author "Monsalve, Mauricio"

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    An Updated Recurrence Model for Chilean Subduction Seismicity and Statistical Validation of Its Poisson Nature
    (2019) Poulos Campbell, Alan John; Monsalve, Mauricio; Zamora, Natalia; Llera Martin, Juan Carlos de la
    Earthquake recurrence models are the basis of seismic hazard analysis and seismic risk evaluation of physical infrastructure. They are based on statistical analysis of earthquake occurrence data available in a specific geographical region. This work proposes a new earthquake recurrence model for the interface and intraslab seismicity of the subduction margin along Chile. The model improves some of the shortcomings of previous available models in the region such as the lack of earthquake declustering or the use of magnitude scales inconsistent with modern ground‐motion prediction equations. Significant differences in seismic rates are found with some previous models. Indeed, the resulting frequencies from the Gutenberg–Richter relations are not only similar to some of the previous works, but also one order of magnitude higher and lower than two of the previously reported models. Because one of the strongest assumptions in earthquake occurrence models is that they follow a homogeneous Poisson process, this hypothesis is statistically tested herein, finding that the declustered catalog only partially complies with this assumption, showing for instance that the interevent times follow approximately an exponential distribution.
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    Data-driven estimation of interdependencies and restoration of infrastructure systems
    (2019) Monsalve, Mauricio; Llera Martin, Juan Carlos de la
    Modern urban systems contain intricate interconnected networks whose components depend on each other to operate, provide value, and sustain a functional society. However, this interconnectedness increases the fragility of these systems by allowing the propagation of disruptions through their interdependencies, which may result in large cascades of failures that can cause severe loss of functionality and recovery capability. Furthermore, the resilience of these systems does not only depend on the individual components, but on their combined ability to recover promptly. With the aim of quantifying the interdependence between these systems, this work introduces a new statistical model for evaluating and simulating the restoration of complex interdependent systems, while modeling their restoration as interdependent processes. The statistical model is introduced along with a custom calibration algorithm that fits the model to observed time series data of infrastructure restoration of functionality. Data from six iconic earthquakes are used to fit and test the model against a suite of service restoration curves associated with different infrastructures. It is concluded that the model may be used to simultaneously estimate the restoration and resilience of an infrastructure system after the disruptions caused by a mainshock. Limitations, possible extensions, and improvements of the model are discussed.
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    Earthquake response sensitivity of complex infrastructure networks
    (2020) Llera Martin, Juan Carlos de la; Monsalve, Mauricio; Ferrario, Elisa; Allen, E.; Chamorro, A.; Castro, S.; Alberto, Yolanda; Arróspide, Felipe; Poulos, Alan; Candia, G.; Aguirre, P.
    Resilience of complex infrastructure networks is critical in achieving earthquake resilience in urban environments. Perhaps due to their modeling complexity, very few research studies have addressed sensitivity of the network response to a severe earthquake hazard field. This research aims to characterize earthquake response sensitivity as a function of different topological parameters of 5 critical complex networks in central Chile, covering the electric, transportation, and drinking water networks. Central Chile was selected because it amounts for almost 50% of the country’s population. What is also particular about this setting, is that the seismic characteristics of the region lead to extended (essentially) N-S strike fault ruptures, which run along the subduction margin defined by the E-W convergence between the South American and Pacific Ocean plates at an unusual rate of about 68 mm/year, thus involving in the strong-motion hazard field geographic scales in the hundreds of kilometers. It is concluded that node and link topological structures differ considerably between these complex systems, which are characterized by several different well-known centrality parameters and other interesting indices and network-class discriminators. Secondly, a component criticality analysis under an earthquake hazard field is also presented just in terms of connectivity/service loss, which enables, at least, a rough identification of the robustness of each network as nodes and links are removed. Results from these topological analyses are useful to identify which components are essential in generating larger earthquake resilience. This is the first time such results are obtained for central Chile using very detailed models of these complex networks
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    Identifying critical components in power distribution networks using graph theoretical measures
    (2019) Llera Martin, Juan Carlos de la; Monsalve, Mauricio
    Critical infrastructures (CIs) are spatially distributed infrastructure networks that supply essential services and goods to our society. However, CIs are exposed to diverse disruptive and severe natural events, random failures and manmade attacks, which undermine their proper functioning producing negative impacts on the population. In this context, it is of paramount importance to identify which are the most relevant CI components that deserve special attention for improving CIs protection. The identification of critical components is a well consolidated practice in risk analysis of complex technological systems; however, when looking at real-world spatially-distributed CIs, identifying the most critical components precisely is challenging. Indeed, CIs cover large spatial areas, have variable degrees of redundancy, and often exhibit irregular topological characteristics. In order to address this problem, this work looks at the ability of different graph theoretical measures or scores at identifying critical components in power distribution networks to later compare them. The scores considered herein include various network centrality measures (degree, eigenvector, closeness, betweenness) and some variations. The comparison of these measures is performed on three power distribution networks from central Chile, finding that a variant of betweenness centrality is the best at identifying the most critical components.
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    Risk and resilience monitor: development of multiscale and multilevel indicators for disaster risk management for the communes and urban areas of Chile
    (2018) González, Daniela P.; Monsalve, Mauricio; Moris Iturrieta, Roberto; Herrera Barriga, Cristóbal