1,721,202 research outputs found
Systemic Vulnerability and Risk Assessment of Transportation Systems Under Natural Hazards Towards More Resilient and Robust Infrastructures
No full textAbstractTransportation infrastructures are complex systems of various connected components like bridges, roads, tunnels, embankments, retaining walls in case of a highway system or wharfs, cranes, buildings, utility systems in case of port facilities. Due to their spatial extent, they are exposed to variable natural hazards such as earthquakes, tsunami or landslides. Experience from past disastrous events shows that transportation infrastructures are quite vulnerable due to the lack of redundancy, the lengthy repair time, the rerouting difficulties or the cascading failures and interdependencies. Their damage could be greatly disruptive in terms of safety of life, business disruption, access to emergency services and key lifelines utilities, rescue operations and socio-economic impacts. Therefore, in terms of resilience it is important to recognize and quantify the risks and global losses associated to damages of transportation systems and to establish efficient risk mitigation strategies. These include, among others, enhancement of emergency preparedness, strengthening of existing structures and improvement of the recovery planning.Herein an integrated framework for the probabilistic systemic vulnerability and risk assessment of transportation and utility networks is presented, based on the achievements of the recently completed EC project SYNER-G (www.syner-g.eu) and the ongoing EC project STREST (www.strest-eu.org). The infrastructure is modeled according to a detailed taxonomy. The framework encompasses in an integrated fashion all aspects in the chain, from regional hazard to fragility assessment of components to the socio-economic impacts of a natural disaster, accounting for relevant uncertainties within an efficient quantitative simulation scheme, and modeling interactions between multiple component systems in the taxonomy. Selected Performance Indicators (PIs) are calculated for each network based on the estimated damages and functionality losses of the different components under the given hazards.The methodology and tools are demonstrated through case studies in the road network and the harbor of Thessaloniki city, Greece, under seismic hazard and associated geotechnical hazards (i.e. soil liquefaction). The applications include assessments of systems’ performance considering the spatial seismic hazard with correlation of ground motion intensities, the vulnerability of the network components, and the effect of interactions within the system, as well as, between components of different systems. In particular, road disruptions can be caused due to direct damage of road segments and bridges, as well as building and overpass collapses. Harbor operations can be disturbed due to failures of waterfront structures and cargo handling equipment, as well as disruptions to the electric power supply and building collapses. The systemic risk for the road network and harbor is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake) and the risk curves (i.e. mean annual rates of exceedance for loss in performance of the infrastructures) are provided. The significant elements for the functionality of each system are defined through correlation factors to the system PIs. Such results can contribute to the decision-making regarding the enhancement of existing and the robust development of new infrastructures in the frame of safety and resiliency
Long-term multi-risk assessment: statistical treatment of interaction among risks
No full textMulti-risk approaches have been recently proposed to assess and compare
different risks in the same target area. The key points of multi-risk assessment are the
development of homogeneous risk definitions and the treatment of risk interaction. The
lack of treatment of interaction may lead to significant biases and thus to erroneous risk
hierarchization, which is one of primary output of risk assessments for decision makers. In
this paper, a formal statistical model is developed to treat interaction between two different
hazardous phenomena in long-term multi-risk assessments, accounting for possible effects
of interaction at hazard, vulnerability and exposure levels. The applicability of the
methodology is demonstrated through two illustrative examples, dealing with the influence
of (1) volcanic ash in seismic risk and (2) local earthquakes in tsunami risk. In these
applications, the bias in single-risk estimation induced by the assumption of independence
among risks is explicitly assessed. An extensive application of this methodology at
regional and sub-regional scale would allow to identify when and where a given interaction
has significant effects in long-term risk assessments, and thus, it should be considered in
multi-risk analyses and risks hierarchization.Published701-7224.2. TTC - Modelli per la stima della pericolosità sismica a scala nazionaleJCR Journalope
Probabilistic seismic hazard assessment: Combining Cornell-like approaches and data at sites through Bayesian inference
No full textThe societal importance and implications of seismic-hazard assessment forces the scientific community to pay increasing attention to the evaluation of uncertainty in order to provide accurate assessments. Probabilistic seismic hazard assessment (PSHA) formally accounts for the natural variability of the involved phenomena, from seismic sources to wave propagation. Recently, increased attention has been paid to the consequences of alternative modeling procedures on hazard results. This uncertainty, essentially of epistemic nature, has been shown to have major impacts on PSHA results, leading to extensive applications of techniques like the logic tree. Here, we develop a formal Bayesian inference scheme for PSHA that allows us, on the one hand, to explicitly account for all uncertainties and, on the other hand, to consider a larger set of sources of information, from heterogeneous models to past data. This process decreases the chance of undesirable biases and leads to a controlled increase of the precision of the probabilistic assessment. In addition, the proposed Bayesian scheme allows (1) the assignment of a subjective reliability to single models, without requirement of completeness or homogeneity, and (2) a transparent and uniform evaluation of the strength of each piece of information used on the final results. The applicability of the method is demonstrated through the assessment of seismic hazard in the Emilia-Romagna region of northern Italy. In this application the results of a traditional Cornell-McGuire hazard model based on a logic tree are updated with the historical macroseismic records to provide a unified assessment that accounts for both sources of information
Considering uncertainties in the determination of earthquake source parameters from seismic spectra
No full textIn this paper, we present a method for handling uncertainties in the determination of the source parameters of earthquakes from spectral data. We propose a robust framework for estimating earthquake source parameters and relative uncertainties, which are propagated down to the estimation of basic seismic parameters of interest such as the seismic moment, the moment magnitude, the source size and the static stress drop. In practice, we put together a Bayesian approach for model parameter estimation and a weighted statistical mixing of multiple solutions obtained from a network of instruments, providing a useful framework for extracting meaningful data from intrinsically uncertain data sets. The Bayesian approach used to estimate the source spectra parameters is a simple but powerful mechanism for non-linear model fitting, providing also the opportunity to naturally propagate uncertainties and to assess the quality and uniqueness of the solution. Another important added value of such an approach is the possibility of integrating information from the expertise of seismologists. Such data can be encoded in a prior state of information that is then updated with the information provided by seismological data. The performance of the proposed approach is demonstrated analysing data from the 1909 April 23 earthquake occurred near Benavente (Portugal)
A unified probabilistic framework for volcanic hazard and eruption forecasting
No full textThe main purpose of this article is to emphasize the importance of clarifying the probabilistic framework adopted for volcanic hazard and eruption forecasting. Eruption forecasting and volcanic hazard analysis seek to quantify the deep uncertainties that pervade the modeling of pre-, sin-, and post-eruptive processes. These uncertainties can be differentiated into three fundamental types: (1) the natural variability of volcanic systems, usually represented as stochastic processes with parameterized distributions (aleatory variability); (2) the uncertainty in our knowledge of how volcanic systems operate and evolve, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (3) the possibility that our forecasts are wrong owing to behaviors of volcanic processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). Here we put forward a probabilistic framework for hazard analysis recently proposed by Marzocchi and Jordan (2014), which unifies the treatment of all three types of uncertainty. Within this framework, an eruption forecasting or a volcanic hazard model is said to be complete only if it (a) fully characterizes the epistemic uncertainties in the model's representation of aleatory variability and (b) can be unconditionally tested (in principle) against observations to identify ontological errors. Unconditional testability, which is the key to model validation, hinges on an experimental concept that characterizes hazard events in terms of exchangeable data sequences with well-defined frequencies. We illustrate the application of this unified probabilistic framework by describing experimental concepts for the forecasting of tephra fall from Campi Flegrei. Eventually, this example may serve as a guide for the application of the same probabilistic framework to other natural hazards
Integration of stochastic models for long-term eruption forecasting into a Bayesian event tree scheme: A basis method to estimate the probability of volcanic unrest
No full textEruption forecasting refers, in general, to the assessment of the occurrence probability of a given eruptive event, whereas volcanic hazards are normally associated with the analysis of superficial and evident phenomena that usually accompany eruptions (e. g., lava, pyroclastic flows, tephra fall, lahars, etc.). Nevertheless, several hazards of volcanic origin may occur in noneruptive phases during unrest episodes. Among others, remarkable examples are gas emissions, phreatic explosions, ground deformation, and seismic swarms. Many of such events may lead to significant damages, and for this reason, the "risk" associated to unrest episodes could not be negligible with respect to eruption-related phenomena. Our main objective in this paper is to provide a quantitative framework to calculate probabilities of volcanic unrest. The mathematical framework proposed is based on the integration of stochastic models based on the analysis of eruption occurrence catalogs into a Bayesian event tree scheme for eruption forecasting and volcanic hazard assessment. Indeed, such models are based on long-term eruption catalogs and in many cases allow a more consistent analysis of long-term temporal modulations of volcanic activity. The main result of this approach is twofold: first, it allows to make inferences about the probability of volcanic unrest; second, it allows to project the results of stochastic modeling of the eruptive history of a volcano toward the probabilistic assessment of volcanic hazards. To illustrate the performance of the proposed approach, we apply it to determine probabilities of unrest at Miyakejima volcano, Japan. © 2013 Springer-Verlag Berlin Heidelberg
In Memoriam: Selva J. Raj (1952-2008)
Full text linkSelva J. Raj was a man dedicated to life and work in all its breathtaking complexity. At the time of his untimely death of a heart attack on March 14th, he was the Stanley S. Kresge Endowed Professor and Chair of the Religious Studies Department at Albion College. In addition to tirelessly serving on a lengthy list of campus-wide, national, and international committees and boards, Selva was co-chair of the Comparative Studies in Religion Section of the AAR since 2001; he rekindled the Conference on the Study of Religions of India in 2004 and served as its annual organizer; he was president of the Midwest AAR from 2003-2005 and president of the Society for Hindu-Christian Studies from 2000-2002
Efecto de las Promociones del Precio Sobre la Venta de Productos no Promocionados: Papel Moderador del Tipo de Vista del Cliente
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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