Safety and Reliability – Safe Societies in a Changing World

Haugen, Stein

doi:10.1201/9781351174664

Cited by 70 publications

(24 citation statements)

References 72 publications

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“…Seven of the eight functions in (40) are well-known test examples, while f 3 is a test function developed by Strömberg (2016Strömberg ( , 2018aStrömberg ( , 2018bStrömberg ( , 2019 in order to evaluate algorithms for reliability-based design optimization. The fourth function f 4 is the well-known peaks function implemented in MATLAB R , where the complete analytical expression can be found easily in the documentation of MATLAB R .…”

Section: Examplesmentioning

confidence: 99%

“…The use of metamodels, such as Kriging in Kleijnen (2009), radial basis function networks in Amouzgar and Strömberg (2017), polynomial chaos expansion in Sudret (2008), support vector machines in Strömberg (2018a) and support vector regression in Yun, Yoon, and Nakayama (2009), for approximating computationally expensive models, such as nonlinear finite element models, in order to perform advanced studies, such as different disciplines of design optimization studied by, for example, Strömberg and Tapankov (2012) and Amouzgar, Rashid, and Strömberg (2013), is today a most established approach in engineering, see for example the review by Wang and Shan (2006). The best choice of metamodel depends strongly on the character of the response, the choice of design of experiments and the definition of best.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of optimal linear, affine and convex combinations of metamodels

Strömberg

2020

Engineering Optimization

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In this article, five different formulations for establishing optimal ensembles of metamodels are presented and compared. The comparison is done by minimizing different norms of the residual vector of the leave-oneout cross-validation errors for linear, affine and convex combinations of 10 metamodels. The norms are taken to be the taxicab, the Euclidean and the infinity norm, respectively. The ensemble of metamodels consists of quadratic regression, Kriging with linear or quadratic bias, radial basis function networks with a-priori linear or quadratic bias, radial basis function networks with a-posteriori linear or quadratic bias, polynomial chaos expansion, support vector regression and least squares support vector regression. Eight benchmark functions are studied as 'black-boxes' using Halton and Hammersley samplings. The optimal ensembles are established for either one of the samplings and then the corresponding root mean square errors are established using the other sampling and vice versa. In total, 80 different test cases (5 formulations, 8 benchmarks and 2 samplings) are studied and presented. In addition, an established design optimization problem is solved using affine and convex combinations. It is concluded that minimization of the taxicab or Euclidean norm of the residual vector of the leave-one-out cross-validation errors for convex combinations of metamodels produces the best ensemble of metamodels.

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Section: Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of optimal linear, affine and convex combinations of metamodels

Strömberg

2020

Engineering Optimization

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“…Zdroje rizik vznikají hlavně na rozhraních: stroj -IT; IT -IT; člověk -IT; např. články v publikaci [34].…”

Section: Příčiny Rizik Technických Dělunclassified

“…organizační havárie. Lidský faktor jako původce havárií z první skupiny je sledován v pracích autorky [3,19]; a velmi podrobně v článcích uvedených v knihách [25][26][27][28][29][30][31][32][33][34].…”

Section: Organizační Havárieunclassified

Analýza, řízení a vypořádání rizik spojených s technickými díly

Procházková¹

2018

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2.1. Systém a pojetí technických děl 2.2. Model technických děl a jejich okolí 2.3. Soubor pojmů 3. ZDROJE RIZIK A BEZPEČNOST TECHNICKÝCH DĚL 3.1. Příčiny rizik technických děl 3.1.1. Procesy vyvolávající jevy, jež jsou zdroji rizik 3.1.2. Havárie technických děl 3.1.3. Selhání obslužnosti technických děl a infrastruktur 3.1.4. Organizační havárie 3.2. Fyzikální podstata průřezových rizik 3.2.1. Zranitelnosti technických děl vyvolané vzájemnými závislostmi 3.2.2. Náhodné a znalostní nejistoty 3.3. Riziko a bezpečnost technických děl a jejich okolí 4. RECENTNÍ POZNATKY SPOJENÉ S METODAMI POUŽÍVANÝMI V INŽENÝRSKÝCH DISCIPLÍNÁCH, KTERÉ PRACUJÍ S RIZIKY 4.1. Přístupy používané při práci s riziky 4.2. Přehled základních používaných metod při práci s riziky a jejich validita 4.2.1. Druhy rizik sledované u technických děl 4.2.2. Metody, techniky a nástroje pro práci s riziky používané v praxi 4.2.3. Metody používané pro analýzu a hodnocení rizik 4.2.3.1. Stručná charakteristika vybraných tradičních metod 4.2.3.2. Tradiční metody a jejich použitelnost při práci s riziky technických děl 4.2.3.3. Doplňující údaje k tradičním nástrojům práce s riziky 4.3. Rizika SoS a jejich řízení SUMMARYSubmitted work "Analysis, management and trade-off with risks connected with technical facilities" deals with the risks associated with the technical facilities, particularly with the complex technical facilities. It demonstrates the ways of work with risks at risk identification, analysis, assessment, management and putting under control aimed to the safety of both, the technical facilities and their surroundings, and simultaneously respecting the current knowledge that the risks are locally and timespecific. The safety is understood as a property of the whole technical facility, which is determined by the quality of the file of anthropogenic measures and activities aimed at the safe technical facility, and even at its critical conditions. Therefore, at safety make up, the publication proposes to monitor both, the public assets and the technical facility assets, and to consider the diversity of their physical natures, vulnerabilities, and the constituent changes over time; which means continuously to solve emerging conflicts.Since the risks are the cause of the technical facilities criticalities in the processes of the sitting, construction, operation and removal of technical facilities with regard to public assets, so the considered goal is ensuring the coexistence of technical facility with the surroundings, i.e. with public assets, which include human life, health and security, property, public welfare, the environment, other technologies, and infrastructures. With regard to the dynamic development of the world, it is necessary to monitor all priority risks and to implement their management and bringing under control with regard to improving or at least maintaining the technical facilities safety at an acceptable level. This means building the safety management systems of technical facilities that respect at work with risks the variabi...

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“…Estimates of data-generated revenue range from tens of billions to over 160 billion USD in 2018 (Statista, 2018;IDC, 2018) and may be broken down by market size and value added, such as the number of jobs created, cost savings, and efficiency and productivity gains (Günther et al, 2017). Teasing out the contribution of Earth and environmental obser-vation data to these revenue estimates is difficult, however, it is important to note that some of the most valuable uses of environmental data are in emergency response management (Dubrow, 2018;Pinelli et al, 2018), drought monitoring (Bernknopf, et al, 2019), pollution assessment, agricultural production and groundwater quality (Forney, et al, 2012), fisheries and water management, logistics and trade, and on longer time scales, in real estate development, risk assessment and the insurance industry (Voosen, 2017;Downs, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Discussion of Value Metrics for Data Repositories in Earth and Environmental Sciences

Parr

Gries

O'Brien

et al. 2019

CODATA

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Despite growing recognition of the importance of public data to the modern economy and to scientific progress, long-term investment in the repositories that manage and disseminate scientific data in easily accessible-ways remains elusive. Repositories are asked to demonstrate that there is a net value of their data and services to justify continued funding or attract new funding sources. Here, representatives from a number of environmental and Earth science repositories evaluate approaches for assessing the costs and benefits of publishing scientific data in their repositories, identifying various metrics that repositories typically use to report on the impact and value of their data products and services, plus additional metrics that would be useful but are not typically measured. We rated each metric by (a) the difficulty of implementation by our specific repositories and (b) its importance for value determination. As managers of environmental data repositories, we find that some of the most easily obtainable data-use metrics (such as data downloads and page views) may be less indicative of value than metrics that relate to discoverability and broader use. Other intangible but equally important metrics (e.g., laws or regulations impacted, lives saved, new proposals generated), will require considerable additional research to describe and develop, plus resources to implement at scale. As value can only be determined from the point of view of a stakeholder, it is likely that multiple sets of metrics will be needed, tailored to specific stakeholder needs. Moreover, economically based analyses or the use of specialists in the field are expensive and can happen only as resources permit.

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Safety and Reliability – Safe Societies in a Changing World

Cited by 70 publications

References 72 publications

Comparison of optimal linear, affine and convex combinations of metamodels

Comparison of optimal linear, affine and convex combinations of metamodels

Analýza, řízení a vypořádání rizik spojených s technickými díly

A Discussion of Value Metrics for Data Repositories in Earth and Environmental Sciences

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