Calibration and Validation of a Seismic Damage Propagation Model for Interdependent Infrastructure Systems

2013 ◽  
Vol 29 (3) ◽  
pp. 1021-1041 ◽  
Author(s):  
Jason Wu ◽  
Leonardo Dueñas-Osorio
Keyword(s):  
Computational Models ◽  
Influential Factors ◽  
Propagation Model ◽  
Power Networks ◽  
Limit States ◽  
Infrastructure Systems ◽  
Calibration And Validation ◽  
Lifeline System ◽  
Theoretical And Computational Models ◽  
Chile Earthquake

Barring a few exceptions, most theoretical and computational models of lifeline system fragility and interdependent response to extreme events still lack calibration and validation relative to real events. This paper expands on this area by evaluating and calibrating a recently proposed Interdependence Fragility Algorithm ( IFA) against field data observed after the 2010 Mw 8.8 offshore Maule, Chile, earthquake. This evaluation incorporates available and simulated properties of the Concepción and Talcahuano water and power networks to try to replicate their topology and seismic response, considering both direct damage and interdependent effects. The calibrated IFA predicts that the probabilities of exceeding the observed high connectivity losses of 0.70 (power) and 0.82 (water), if taken as limit states, are 97% and 72%, respectively. These predictions capture complex interdependent lifeline system responses reasonably well and reveal influential factors for IFA model accuracy and uncertainty reduction, enabling reliable planning, design, expansion, and maintenance of infrastructure systems in practice.

Fo and Ni Relations in Olivine Differentiate between Crystallization and Diffusion Trends

2020 ◽  
Author(s):  
Boris Gordeychik ◽  
Tatiana Churikova ◽  
Thomas Shea ◽  
Andreas Kronz ◽  
Alexander Simakin ◽  
...  
Keyword(s):  
Diffusion Coefficients ◽  
Computational Models ◽  
Three Dimensional ◽  
Crystal Shape ◽  
Diffusion Anisotropy ◽  
Progressive Loss ◽  
Theoretical And Computational Models ◽  
Diffusion Controlled ◽  
Mafic Magmas ◽  
And Diffusion

Abstract Nickel is a strongly compatible element in olivine, and thus fractional crystallization of olivine typically results in a concave-up trend on a Fo–Ni diagram. ‘Ni-enriched’ olivine compositions are considered those that fall above such a crystallization trend. To explain Ni-enriched olivine crystals, we develop a set of theoretical and computational models to describe how primitive olivine phenocrysts from a parent (high-Mg, high-Ni) basalt re-equilibrate with an evolved (low-Mg, low-Ni) melt through diffusion. These models describe the progressive loss of Fo and Ni in olivine cores during protracted diffusion for various crystal shapes and different relative diffusivities for Ni and Fe–Mg. In the case when the diffusivity of Ni is lower than that for Fe–Mg interdiffusion, then olivine phenocrysts affected by protracted diffusion form a concave-down trend that contrasts with the concave-up crystallization trend. Models for different simple geometries show that the concavity of the diffusion trend does not depend on the size of the crystals and only weakly depends on their shape. We also find that the effect of diffusion anisotropy on trend concavity is of the same magnitude as the effect of crystal shape. Thus, both diffusion anisotropy and crystal shape do not significantly change the concave-down diffusion trend. Three-dimensional numerical diffusion models using a range of more complex, realistic olivine morphologies with anisotropy corroborate this conclusion. Thus, the curvature of the concave-down diffusion trend is mainly determined by the ratio of Ni and Fe–Mg diffusion coefficients. The initial and final points of the diffusion trend are in turn determined by the compositional contrast between mafic and more evolved melts that have mixed to cause disequilibrium between olivine cores and surrounding melt. We present several examples of measurements on olivine from arc basalts from Kamchatka, and published olivine datasets from mafic magmas from non-subduction settings (lamproites and kimberlites) that are consistent with diffusion-controlled Fo–Ni behaviour. In each case the ratio of Ni and Fe–Mg diffusion coefficients is indicated to be <1. These examples show that crystallization and diffusion can be distinguished by concave-up and concave-down trends in Fo–Ni diagrams.

Socioeconomic Impacts of Infrastructure Disruptions

2016 ◽  
Author(s):  
Stephanie E. Chang
Keyword(s):  
Electric Power ◽  
Computational Models ◽  
Critical Infrastructure ◽  
Secondary Data ◽  
Disaster Mitigation ◽  
Economic Activities ◽  
Physical Damage ◽  
Socioeconomic Impacts ◽  
Infrastructure Systems ◽  
Large Populations

Infrastructure systems—sometimes referred to as critical infrastructure or lifelines—provide services such as energy, water, sanitation, transportation, and communications that are essential for social and economic activities. Moreover, these systems typically serve large populations and comprise geographically extensive networks. They are also highly interdependent, so outages in one system such as electric power or telecommunications often affect other systems. As a consequence, when infrastructure systems are damaged in disasters, the ensuing losses are often substantial and disproportionately large. Collapse of a single major bridge, for example, can disrupt traffic flows over a broad region and impede emergency response, evacuation, commuting, freight movement, and economic recovery. Power outages in storms and other hazard events can affect millions of people, shut down businesses, and even cause fatalities. Infrastructure outages typically last from hours to weeks but can extend for months or even years. Minimizing disruptions to infrastructure services is thus key to enhancing communities’ disaster resilience. Research on infrastructure systems in natural hazards has been growing, especially as major disasters provide new data, insights, and urgency to the problem. Engineering advances have been made in understanding how hazard stresses may damage the physical components of infrastructure systems such as pipes and bridges, as well as how these elements can be designed to better withstand hazards. Modeling studies have assessed how physical damage disrupts the provision of services—for example, by indicating which neighborhoods in an urban area may be without potable water—and how disruption can be reduced through engineering and planning. The topic of infrastructure interdependencies has commanded substantial research interest. Alongside these developments, social science and interdisciplinary research has also been growing on the important topic of how infrastructure disruption in disasters has affected populations and economies. Insights into these impacts derive from a variety of information sources, including surveys, field observations, analysis of secondary data, and computational models. Such research has established the criticality of electric power and water services, for example, and the heightened vulnerability of certain population groups to infrastructure disruption. Omitting the socioeconomic impacts of infrastructure disruptions can lead to underinvestment in disaster mitigation. While the importance of understanding and reducing infrastructure disruption impacts is well-established, many important research gaps remain.

scholarly journals Modelling the Spread of Botnet Malware in IoT-Based Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Dilara Acarali ◽  
Muttukrishnan Rajarajan ◽  
Nikos Komninos ◽  
B. B. Zarpelão
Keyword(s):  
Real Life ◽  
Influential Factors ◽  
Propagation Model ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Processing Power ◽  
The Monte Carlo Method ◽  
Attack Surface ◽  
The Impact

The propagation approach of a botnet largely dictates its formation, establishing a foundation of bots for future exploitation. The chosen propagation method determines the attack surface and, consequently, the degree of network penetration, as well as the overall size and the eventual attack potency. It is therefore essential to understand propagation behaviours and influential factors in order to better secure vulnerable systems. Whilst botnet propagation is generally well studied, newer technologies like IoT have unique characteristics which are yet to be thoroughly explored. In this paper, we apply the principles of epidemic modelling to IoT networks consisting of wireless sensor nodes. We build IoT-SIS, a novel propagation model which considers the impact of IoT-specific characteristics like limited processing power, energy restrictions, and node density on the formation of a botnet. Focusing on worm-based propagation, this model is used to explore the dynamics of spread using numerical simulations and the Monte Carlo method to discuss the real-life implications of our findings.

Using Theoretical and Computational Models to Understand How Metals Function as Temperature Sensors

2016 ◽  
pp. 668-702
Author(s):  
Fred Lacy
Keyword(s):  
Experimental Data ◽  
Electrical Conductivity ◽  
Electrical Resistivity ◽  
Computational Models ◽  
Theoretical Models ◽  
Temperature Sensors ◽  
Atomic Level ◽  
Atomic Motion ◽  
Temperature Changes ◽  
Theoretical And Computational Models

Electrical conductivity is a basic property of materials that determines how well the material conducts electricity. However, models are needed that help explain how conductors function as their size and temperature changes. This research demonstrates and explains how important atomic motion is in understanding electrical conductivity for conductors (and thus the ability of metals to function as temperature sensors). A derivation is performed (on an atomic level) that provides a theoretical relationship between electrical resistivity, temperature, and material thickness. Subsequently, computational models are used to determine the optimal parameters for the theoretical models as well as the conditions under which they are accurate. Comparisons are performed using experimental data showing that the models are valid and accurate.

scholarly journals Quantification of Lifeline System Interdependencies after the 27 February 2010 Mw 8.8 Offshore Maule, Chile, Earthquake

2012 ◽  
Vol 28 (1_suppl1) ◽  
pp. 581-603 ◽  
Author(s):  
Leonardo Dueñas-Osorio ◽  
Alexis Kwasinski
Keyword(s):  
Power Systems ◽  
Prediction Models ◽  
Lifeline System ◽  
Disaster Data ◽  
Chile Earthquake ◽  
Cross Correlations ◽  
Coupling Strengths ◽  
Lag Times ◽  
Lifeline Systems ◽  
Chilean Earthquake

Data on lifeline system service restoration is seldom exploited for the calibration of performance prediction models or for response comparisons across systems and events. This study explores utility restoration curves after the 2010 Chilean earthquake through a time series method to quantify coupling strengths across lifeline systems. When consistent with field information, cross-correlations from restoration curves without significant lag times quantify operational interdependence, whereas those with significant lags reveal logistical interdependence. Synthesized coupling strengths are also proposed to incorporate cross-correlations and lag times at once. In the Chilean earthquake, coupling across fixed and mobile phones was the strongest per region followed by coupling within and across telecommunication and power systems in adjacent regions. Unapparent couplings were also revealed among telecommunication and power systems with water networks. The proposed methodology can steer new protocols for post-disaster data collection, including anecdotal information to evaluate causality, and inform infrastructure interdependence effect prediction models.

Effects of Information Source, Pedigree, and Reliability on Operators' Utilizaton of Diagnostic Advice

2005 ◽  
Vol 49 (3) ◽  
pp. 487-491 ◽  
Author(s):  
Poornima Madhavan ◽  
Douglas A. Wiegmann
Keyword(s):  
Computational Models ◽  
Decision Aids ◽  
Information Source ◽  
Theoretical And Computational Models ◽  
Luggage Screening

Studies have demonstrated that humans appear to apply norms of humanhuman interaction to their interaction with machines. Yet, there exist subtle differences in peoples' perceptions of automated aids compared to humans. We examined factors differentiating human-human and human-automation interaction, wherein participants (n = 180) performed a luggage-screening task with the assistance of human or automated advisers that differed in pedigree (expert vs. novice) and reliability (high vs. low). Dependence on advice was assessed. Participants agreed more with an automated 'novice' than a human 'novice' suggesting a bias toward automation. Automation biases broke down when automated aids portrayed as 'experts' generated errors, leading to a drop in compliance and reliance on automation relative to humans. The results have implications for the development of theoretical and computational models of optimal user dependence on decision aids.

Sign in / Sign up

Export Citation Format

Share Document