110,264 research outputs found
PEER Testbed Study on a Laboratory Building: Exercising Seismic Performance Assessment
From 2002 to 2004 (years five and six of a ten-year funding cycle), the PEER Center organized
the majority of its research around six testbeds. Two buildings and two bridges, a campus, and a
transportation network were selected as case studies to “exercise” the PEER performance-based
earthquake engineering methodology. All projects involved interdisciplinary teams of
researchers, each producing data to be used by other colleagues in their research. The testbeds
demonstrated that it is possible to create the data necessary to populate the PEER performancebased framing equation, linking the hazard analysis, the structural analysis, the development of
damage measures, loss analysis, and decision variables.
This report describes one of the building testbeds—the UC Science Building. The project
was chosen to focus attention on the consequences of losses of laboratory contents, particularly
downtime. The UC Science testbed evaluated the earthquake hazard and the structural
performance of a well-designed recently built reinforced concrete laboratory building using the
OpenSees platform. Researchers conducted shake table tests on samples of critical laboratory
contents in order to develop fragility curves used to analyze the probability of losses based on
equipment failure. The UC Science testbed undertook an extreme case in performance
assessment—linking performance of contents to operational failure. The research shows the
interdependence of building structure, systems, and contents in performance assessment, and
highlights where further research is needed.
The Executive Summary provides a short description of the overall testbed research
program, while the main body of the report includes summary chapters from individual
researchers. More extensive research reports are cited in the reference section of each chapter
Use of rod compactors for high voltage overhead power lines magnetic field mitigation
In the last decades, strengthening the high voltage transmission system through the installation of new overhead power lines has become critical, especially in highly developed areas. Present laws concerning the human exposure to electric and magnetic fields introduce constraints to be considered in both new line construction and existing systems. In the paper, a technique for passive magnetic field mitigation in areas close to overhead power lines is introduced, fully modelled and discussed through a parametric analysis. The investigated solution, which basically consists in approaching line conductors along the span making use of rod insulators, is applicable on both existing and under-design overhead lines as an alternative to other mitigating actions. Making use of a 3-dimensional representation, the procedure computes both positions of phase conductors and forces acting on insulators, towers, conductors and compactors, with the aim of evaluating the additional mechanical stress introduced by the compactors. Finally, a real case study is reported to demonstrate and quantify the benefits in terms of ground magnetic field reduction achievable by applying the proposed solution, in comparison to a traditional configuration. Furthermore, using compactors to passively reduce the magnetic field is simple to be applied, minimally invasive and quite inexpensive as regards to alternative mitigating actions
Decision aid problems criteria for infrastructure networks vulnerability analysis (regular paper)
Natural disasters through infrastructure networks might aggravate or mitigate consequences to stakes. The objective of this paper is to characterize this kind of situation in order to provide a solid foundation for the decision aid. This characterization includes a description of the typology, actions and potential actions identification, determining preference systems, as well as a set of specific problems to each phase
Assurance Benefits of ISO 26262 compliant Microcontrollers for safety-critical Avionics
The usage of complex Microcontroller Units (MCUs) in avionic systems
constitutes a challenge in assuring their safety. They are not developed
according to the development requirements accepted by the aerospace industry.
These Commercial off-the-shelf (COTS) hardware components usually target other
domains like the telecommunication branch. In the last years MCUs developed in
compliance to the ISO 26262 have been released on the market for safety-related
automotive applications. The avionic assurance process could profit from these
safety MCUs. In this paper we present evaluation results based on the current
assurance practice that demonstrates expected assurance activities benefit from
ISO 26262 compliant MCUs.Comment: Submitted to SafeComp 2018: http://www.es.mdh.se/safecomp2018
Quantifying the Influence of Component Failure Probability on Cascading Blackout Risk
The risk of cascading blackouts greatly relies on failure probabilities of
individual components in power grids. To quantify how component failure
probabilities (CFP) influences blackout risk (BR), this paper proposes a
sample-induced semi-analytic approach to characterize the relationship between
CFP and BR. To this end, we first give a generic component failure probability
function (CoFPF) to describe CFP with varying parameters or forms. Then the
exact relationship between BR and CoFPFs is built on the abstract
Markov-sequence model of cascading outages. Leveraging a set of samples
generated by blackout simulations, we further establish a sample-induced
semi-analytic mapping between the unbiased estimation of BR and CoFPFs.
Finally, we derive an efficient algorithm that can directly calculate the
unbiased estimation of BR when the CoFPFs change. Since no additional
simulations are required, the algorithm is computationally scalable and
efficient. Numerical experiments well confirm the theory and the algorithm
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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