Auswahl der wissenschaftlichen Literatur zum Thema „Propagation risk“
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Zeitschriftenartikel zum Thema "Propagation risk"
Yan, Huining, Hua Li, Qiubai Sun und Yuxi Jiang. „Propagation and control of congestion risk in scale-free networks based on information entropy“. PLOS ONE 19, Nr. 3 (22.03.2024): e0300422. http://dx.doi.org/10.1371/journal.pone.0300422.
Der volle Inhalt der QuelleZhang, Xuejun, Shuaizhe Zhao und Hao Mei. „Analysis of Airport Risk Propagation in Chinese Air Transport Network“. Journal of Advanced Transportation 2022 (01.03.2022): 1–13. http://dx.doi.org/10.1155/2022/9958810.
Der volle Inhalt der QuelleXu, Junke, Jiwei Zhu und Jiancang Xie. „Study on the Evolution of Risk Contagion in Urban River Ecological Management Projects Based on SEIRS“. Water 15, Nr. 14 (19.07.2023): 2622. http://dx.doi.org/10.3390/w15142622.
Der volle Inhalt der QuelleHe, Zhenggang, Jing-Ni Guo und Jun-Xiang Xu. „Cascade Failure Model in Multimodal Transport Network Risk Propagation“. Mathematical Problems in Engineering 2019 (06.12.2019): 1–7. http://dx.doi.org/10.1155/2019/3615903.
Der volle Inhalt der QuelleSong, Yue, Naiding Yang, Yanlu Zhang und Jingbei Wang. „Suppressing risk propagation in R&D networks: the role of government intervention“. Chinese Management Studies 13, Nr. 4 (04.11.2019): 1019–43. http://dx.doi.org/10.1108/cms-07-2018-0621.
Der volle Inhalt der QuelleHan, Yuanwen, Jiang Shen, Xuwei Zhu, Bang An, Fusheng Liu und Xueying Bao. „Study on the Mechanism of Safety Risk Propagation in Subway Construction Projects“. Sustainability 16, Nr. 2 (17.01.2024): 796. http://dx.doi.org/10.3390/su16020796.
Der volle Inhalt der QuelleChen, Tingting, Yan Li, Xiongfei Jiang und Lingjie Shao. „Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks“. Applied Sciences 13, Nr. 2 (14.01.2023): 1129. http://dx.doi.org/10.3390/app13021129.
Der volle Inhalt der QuelleErnst, Oliver G., Alois Pichler und Björn Sprungk. „Wasserstein Sensitivity of Risk and Uncertainty Propagation“. SIAM/ASA Journal on Uncertainty Quantification 10, Nr. 3 (16.08.2022): 915–48. http://dx.doi.org/10.1137/20m1325459.
Der volle Inhalt der QuelleFuh, Cheng-Der, und Chu-Lan Michael Kao. „Credit Risk Propagation in Structural-Form Models“. SIAM Journal on Financial Mathematics 12, Nr. 4 (Januar 2021): 1340–73. http://dx.doi.org/10.1137/20m135340x.
Der volle Inhalt der QuelleCui, Bo, Meilong Le und Jinfu Zhu. „Review of the network risk propagation research“. Aeronautics and Aerospace Open Access Journal 3, Nr. 2 (13.05.2019): 66–74. http://dx.doi.org/10.15406/aaoaj.2019.03.00082.
Der volle Inhalt der QuelleDissertationen zum Thema "Propagation risk"
Garg, Tushar. „Estimating change propagation risk using TRLs and system architecture“. Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/110134.
Der volle Inhalt der QuelleCataloged from PDF version of thesis.
Includes bibliographical references (pages 27-28).
Risk estimation is a key activity for product development and technology integration programs. There are a number of decision support tools that help project managers identify and mitigate risks in a project, however few explicitly consider the effects of architecture on risk. We propose a novel risk estimation framework that includes considerations of the system architecture. By starting with traditional project management literature, we define risk as a combination of likelihood and impact. We use Technology Readiness Levels as our measure for likelihood, and given that change propagates through interfaces, we used metrics that relate to connectivity to estimate impact. To analyze the connectivity, we model systems using networks of nodes and edges and calculate centrality metrics. This framework is applied to an industry example and we visualize the data in different formats to aid in analysis. The insights gained from this analysis are discussed, and we conclude that the risk estimation framework provides estimates that are in line with the experience of engineers at the company.
by Tushar Garg.
S.M. in Engineering and Management
Selda, Konukcu. „Application Of Risk Management Process On Wave Propagation In Aerospace Medium“. Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607606/index.pdf.
Der volle Inhalt der QuelleGhadge, Abhijeet. „A systems thinking approach for modelling supply chain risk propagation“. Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13561.
Der volle Inhalt der QuelleKumar, Vikas. „Soft computing approaches to uncertainty propagation in environmental risk mangement“. Doctoral thesis, Universitat Rovira i Virgili, 2008. http://hdl.handle.net/10803/8558.
Der volle Inhalt der QuelleIn the first part of this thesis different uncertainty propagation methods have been investigated. The first methodology is generalized fuzzy α-cut based on the concept of transformation method. A case study of uncertainty analysis of pollutant transport in the subsurface has been used to show the utility of this approach. This approach shows superiority over conventional methods of uncertainty modelling. A Second method is proposed to manage uncertainty and variability together in risk models. The new hybrid approach combining probabilistic and fuzzy set theory is called Fuzzy Latin Hypercube Sampling (FLHS). An important property of this method is its ability to separate randomness and imprecision to increase the quality of information. A fuzzified statistical summary of the model results gives indices of sensitivity and uncertainty that relate the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method is validated to analyze total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in Basque Country, Spain.
The second part of this thesis deals with the use of artificial intelligence technique for generating environmental indices. The first paper focused on the development of a Hazzard Index (HI) using persistence, bioaccumulation and toxicity properties of a large number of organic and inorganic pollutants. For deriving this index, Self-Organizing Maps (SOM) has been used which provided a hazard ranking for each compound. Subsequently, an Integral Risk Index was developed taking into account the HI and the concentrations of all pollutants in soil samples collected in the target area. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a Geographic Information System (GIS). The second paper is an improvement of the first work. New approach called Neuro-Probabilistic HI was developed by combining SOM and Monte-Carlo analysis. It considers uncertainty associated with contaminants characteristic values. This new index seems to be an adequate tool to be taken into account in risk assessment processes. In both study, the methods have been validated through its implementation in the industrial chemical / petrochemical area of Tarragona.
The third part of this thesis deals with decision-making framework for environmental risk management. In this study, an integrated fuzzy relation analysis (IFRA) model is proposed for risk assessment involving multiple criteria. The fuzzy risk-analysis model is proposed to comprehensively evaluate all risks associated with contaminated systems resulting from more than one toxic chemical. The model is an integrated view on uncertainty techniques based on multi-valued mappings, fuzzy relations and fuzzy analytical hierarchical process. Integration of system simulation and risk analysis using fuzzy approach allowed to incorporate system modelling uncertainty and subjective risk criteria. In this study, it has been shown that a broad integration of fuzzy system simulation and fuzzy risk analysis is possible.
In conclusion, this study has broadly demonstrated the usefulness of soft computing approaches in environmental risk analysis. The proposed methods could significantly advance practice of risk analysis by effectively addressing critical issues of uncertainty propagation problem.
Los problemas del mundo real, especialmente aquellos que implican sistemas naturales, son complejos y se componen de muchos componentes indeterminados, que muestran en muchos casos una relación no lineal. Los modelos convencionales basados en técnicas analíticas que se utilizan actualmente para conocer y predecir el comportamiento de dichos sistemas pueden ser muy complicados e inflexibles cuando se quiere hacer frente a la imprecisión y la complejidad del sistema en un mundo real. El tratamiento de dichos sistemas, supone el enfrentarse a un elevado nivel de incertidumbre así como considerar la imprecisión. Los modelos clásicos basados en análisis numéricos, lógica de valores exactos o binarios, se caracterizan por su precisión y categorización y son clasificados como una aproximación al hard computing. Por el contrario, el soft computing tal como la lógica de razonamiento probabilístico, las redes neuronales artificiales, etc., tienen la característica de aproximación y disponibilidad. Aunque en la hard computing, la imprecisión y la incertidumbre son propiedades no deseadas, en el soft computing la tolerancia en la imprecisión y la incerteza se aprovechan para alcanzar tratabilidad, bajos costes de computación, una comunicación efectiva y un elevado Machine Intelligence Quotient (MIQ). La tesis propuesta intenta explorar el uso de las diferentes aproximaciones en la informática blanda para manipular la incertidumbre en la gestión del riesgo medioambiental. El trabajo se ha dividido en tres secciones que forman parte de cinco artículos.
En la primera parte de esta tesis, se han investigado diferentes métodos de propagación de la incertidumbre. El primer método es el generalizado fuzzy α-cut, el cual está basada en el método de transformación. Para demostrar la utilidad de esta aproximación, se ha utilizado un caso de estudio de análisis de incertidumbre en el transporte de la contaminación en suelo. Esta aproximación muestra una superioridad frente a los métodos convencionales de modelación de la incertidumbre. La segunda metodología propuesta trabaja conjuntamente la variabilidad y la incertidumbre en los modelos de evaluación de riesgo. Para ello, se ha elaborado una nueva aproximación híbrida denominada Fuzzy Latin Hypercube Sampling (FLHS), que combina los conjuntos de la teoría de probabilidad con la teoría de los conjuntos difusos. Una propiedad importante de esta teoría es su capacidad para separarse los aleatoriedad y imprecisión, lo que supone la obtención de una mayor calidad de la información. El resumen estadístico fuzzificado de los resultados del modelo generan índices de sensitividad e incertidumbre que relacionan los efectos de la variabilidad e incertidumbre de los parámetros de modelo con las predicciones de los modelos. La viabilidad del método se llevó a cabo mediante la aplicación de un caso a estudio donde se analizó la varianza total en la cálculo del incremento del riesgo sobre el tiempo de vida de los habitantes que habitan en los alrededores de una incineradora de residuos sólidos urbanos en Tarragona, España, debido a las emisiones de dioxinas y furanos (PCDD/Fs).
La segunda parte de la tesis consistió en la utilización de las técnicas de la inteligencia artificial para la generación de índices medioambientales. En el primer artículo se desarrolló un Índice de Peligrosidad a partir de los valores de persistencia, bioacumulación y toxicidad de un elevado número de contaminantes orgánicos e inorgánicos. Para su elaboración, se utilizaron los Mapas de Auto-Organizativos (SOM), que proporcionaron un ranking de peligrosidad para cada compuesto. A continuación, se elaboró un Índice de Riesgo Integral teniendo en cuenta el Índice de peligrosidad y las concentraciones de cada uno de los contaminantes en las muestras de suelo recogidas en la zona de estudio. Finalmente, se elaboró un mapa de la distribución espacial del Índice de Riesgo Integral mediante la representación en un Sistema de Información Geográfico (SIG). El segundo artículo es un mejoramiento del primer trabajo. En este estudio, se creó un método híbrido de los Mapas Auto-organizativos con los métodos probabilísticos, obteniéndose de esta forma un Índice de Riesgo Integrado. Mediante la combinación de SOM y el análisis de Monte-Carlo se desarrolló una nueva aproximación llamada Índice de Peligrosidad Neuro-Probabilística. Este nuevo índice es una herramienta adecuada para ser utilizada en los procesos de análisis. En ambos artículos, la viabilidad de los métodos han sido validados a través de su aplicación en el área de la industria química y petroquímica de Tarragona (Cataluña, España).
El tercer apartado de esta tesis está enfocado en la elaboración de una estructura metodológica de un sistema de ayuda en la toma de decisiones para la gestión del riesgo medioambiental. En este estudio, se presenta un modelo integrado de análisis de fuzzy (IFRA) para la evaluación del riesgo cuyo resultado depende de múltiples criterios. El modelo es una visión integrada de las técnicas de incertidumbre basadas en diseños de valoraciones múltiples, relaciones fuzzy y procesos analíticos jerárquicos inciertos. La integración de la simulación del sistema y el análisis del riesgo utilizando aproximaciones inciertas permitieron incorporar la incertidumbre procedente del modelo junto con la incertidumbre procedente de la subjetividad de los criterios. En este estudio, se ha demostrado que es posible crear una amplia integración entre la simulación de un sistema incierto y de un análisis de riesgo incierto.
En conclusión, este trabajo demuestra ampliamente la utilidad de aproximación Soft Computing en el análisis de riesgos ambientales. Los métodos propuestos podría avanzar significativamente la práctica de análisis de riesgos de abordar eficazmente el problema de propagación de incertidumbre.
Fang, Chao. „Modeling and Analysing Propagation Behavior in Complex Risk Network : A Decision Support System for Project Risk Management“. Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-01018574.
Der volle Inhalt der QuelleEsperon, Miguez Manuel. „Financial and risk assessment and selection of health monitoring system design options for legacy aircraft“. Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8062.
Der volle Inhalt der QuelleXuan, Yunqing. „Uncertainty propagation in complex coupled flood risk models using numerical weather prediction and weather radars“. Thesis, University of Bristol, 2007. http://hdl.handle.net/1983/c76c4eb0-9c9e-4ddc-866c-9bbdbfa4ec25.
Der volle Inhalt der QuelleLARI, SERENA. „Multi scale heuristic and quantitative multi-risk assessment in the Lombardy region, with uncertainty propagation“. Doctoral thesis, Università degli Studi di Milano-Bicocca, 2009. http://hdl.handle.net/10281/7550.
Der volle Inhalt der QuelleAkiode, Olukemi Adejoke. „Examination and management of human African Trypanosomiasis propagation using geospatial techniques“. Thesis, Abertay University, 2014. https://rke.abertay.ac.uk/en/studentTheses/9419b401-6604-4530-9938-57ab03234e67.
Der volle Inhalt der QuelleAksen, Ernest, Jacek Cukrowski und Manfred M. Fischer. „Propagation of Crises Across Countries: Trade Roots of Contagion Effects“. WU Vienna University of Economics and Business, 2001. http://epub.wu.ac.at/4235/1/WGI_DP_7801.pdf.
Der volle Inhalt der QuelleSeries: Discussion Papers of the Institute for Economic Geography and GIScience
Bücher zum Thema "Propagation risk"
Ben Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. Risk Propagation Assessment for Network Security. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.
Der volle Inhalt der QuelleGrigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. London: Springer London, 2012.
Den vollen Inhalt der Quelle findenCotaras, Frederick D. Acoustic propagation loss predictions for a site on the Bermuda rise at low and very low frequencies. Dartmouth, N.S: National Defence, Research and Development Branch, 1992.
Den vollen Inhalt der Quelle findenRisk Propagation Assessment for Network Security Focus Series. ISTE Ltd and John Wiley & Sons Inc, 2013.
Den vollen Inhalt der Quelle findenPirovano, Alain, Mohamed Slim Ben Mahmoud und Nicolas Larrieu. Risk Propagation Assessment for Network Security: Application to Airport Communication Network Design. Wiley & Sons, Incorporated, John, 2013.
Den vollen Inhalt der Quelle findenPirovano, Aliain, Nicolas Larrieu und Ben Mahmoud. Risk Propagation Assessment for Network Security: Application to Airport Communication Network Design. Wiley & Sons, Incorporated, John, 2013.
Den vollen Inhalt der Quelle findenPirovano, Alain, Mohamed Slim Ben Mahmoud und Nicolas Larrieu. Risk Propagation Assessment for Network Security: Application to Airport Communication Network Design. Wiley & Sons, Incorporated, John, 2013.
Den vollen Inhalt der Quelle findenPirovano, Alain, Mohamed Slim Ben Mahmoud und Nicolas Larrieu. Risk Propagation Assessment for Network Security: Application to Airport Communication Network Design. Wiley & Sons, Incorporated, John, 2013.
Den vollen Inhalt der Quelle findenPirovano, Alain, Mohamed Slim Ben Mahmoud und Nicolas Larrieu. Risk Propagation Assessment for Network Security: Application to Airport Communication Network Design. Wiley & Sons, Incorporated, John, 2013.
Den vollen Inhalt der Quelle findenGrigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. Springer, 2014.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Propagation risk"
Ben Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. „A Quantitative Network Risk Assessment Methodology Based on Risk Propagation“. In Risk Propagation Assessment for Network Security, 27–39. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.ch3.
Der volle Inhalt der QuelleBen Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. „Security Risk Management Background“. In Risk Propagation Assessment for Network Security, 17–25. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.ch2.
Der volle Inhalt der QuelleMoss, Robb Eric S. „Functions of Random Variables: Error Propagation“. In Applied Civil Engineering Risk Analysis, 61–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22680-0_5.
Der volle Inhalt der QuelleFumagalli, Mattia, Gal Engelberg, Tiago Prince Sales, Ítalo Oliveira, Dan Klein, Pnina Soffer, Riccardo Baratella und Giancarlo Guizzardi. „On the Semantics of Risk Propagation“. In Lecture Notes in Business Information Processing, 69–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33080-3_5.
Der volle Inhalt der QuelleBen Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. „Introduction to Information System Security Risk Management Process“. In Risk Propagation Assessment for Network Security, 1–15. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.ch1.
Der volle Inhalt der QuelleBen Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. „The Aeromacs Communication System in the SESAR Project“. In Risk Propagation Assessment for Network Security, 42–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.ch4.
Der volle Inhalt der QuelleBen Mahmoud, Mohamed Slim, Nicolas Larrieu und Alain Pirovano. „Aeronautical Network Case Study“. In Risk Propagation Assessment for Network Security, 59–107. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579947.ch5.
Der volle Inhalt der QuelleHäring, Ivo. „Hazard Propagation I: Explosions and Blast“. In Risk Analysis and Management: Engineering Resilience, 115–35. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0015-7_6.
Der volle Inhalt der QuelleMa, Mingyuan. „Financial Risk Propagation Model Under Network Technology“. In Application of Intelligent Systems in Multi-modal Information Analytics, 92–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05237-8_12.
Der volle Inhalt der QuelleRotaru, Kristian, und Mehrdokht Pournader. „Modeling Risk Emergence and Propagation in Buyer-Supplier-Customer Relationships“. In Supply Chain Risk Management, 43–63. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4106-8_3.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Propagation risk"
Pacheco, M., und J. F. Vasconcellos. „Probabilistic assessment of leachate propagation in the groundwater by an uncontrolled landfill“. In RISK ANALYSIS 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/risk060191.
Der volle Inhalt der QuellePersson, K., J. Jarsjö, C. Prieto und G. Destouni. „Propagation of environmental risk from contaminant transport through groundwater and stream networks“. In RISK ANALYSIS 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/risk080061.
Der volle Inhalt der QuelleBuchs, Colette, Jocelyn Minini und Stéphane Commend. „Calibration of Highly Computationally Intensive Propagation Models of Flow-Like Natural Hazards“. In Geo-Risk 2023. Reston, VA: American Society of Civil Engineers, 2023. http://dx.doi.org/10.1061/9780784484975.038.
Der volle Inhalt der QuelleGao, L., und L. M. Zhang. „Numerical Simulation of Post-Fire Debris Flow Hazards Using a Triggering-Propagation Model“. In Geo-Risk 2023. Reston, VA: American Society of Civil Engineers, 2023. http://dx.doi.org/10.1061/9780784484968.008.
Der volle Inhalt der QuelleLmoussaoui, H., und H. Jamouli. „Risk Propagation Modeling of Construction Project“. In Proceedings of the 31st European Safety and Reliability Conference. Singapore: Research Publishing Services, 2021. http://dx.doi.org/10.3850/978-981-18-2016-8_192-cd.
Der volle Inhalt der QuelleShabnam, Luba, Farzana Haque, Moshiur Bhuiyan und Aneesh Krishna. „Risk Measure Propagation through Organisational Network“. In 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2014. http://dx.doi.org/10.1109/compsacw.2014.40.
Der volle Inhalt der QuelleWang, Chunsheng, Yifan Wu, Haipeng Si und Lan Duan. „Acoustic emission monitoring of bridge cable wires crack propagation“. In IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/seoul.2020.106.
Der volle Inhalt der QuelleDi Curzio, Diego, und Giovanna Vessia. „Uncertainty Propagation Assessment in CPTu-Based Lithological Modeling Using Stochastic Co-Simulation“. In International Symposium for Geotechnical Safety & Risk. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5182-7_00-04-004.xml.
Der volle Inhalt der QuelleJi, Pengcheng, Li Li und Qingyun Yu. „Risk Propagation Analysis of Complex Manufacturing System Based on Virus Propagation Model“. In 2023 China Automation Congress (CAC). IEEE, 2023. http://dx.doi.org/10.1109/cac59555.2023.10451520.
Der volle Inhalt der QuelleWang, Chang-guang, Shuai Fu, Xu Bai und Li-jing Bai. „Risk Perception in Modeling Malware Propagation in Networks“. In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.115.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Propagation risk"
Hohmann, Matthew, und Wade Wall. Operational-scale demonstration of propagation protocols and comparative demographic monitoring for reintroducing five southeastern endangered and at-risk plants : final report. Construction Engineering Research Laboratory (U.S.), März 2018. http://dx.doi.org/10.21079/11681/26525.
Der volle Inhalt der QuelleWeeks, Timothy "Dash". DTPH56-13-X-000013 Modern High-Toughness Steels for Fracture Propagation and Arrest Assessment-Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2018. http://dx.doi.org/10.55274/r0012037.
Der volle Inhalt der QuelleVecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond und Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.
Der volle Inhalt der QuelleBain, Rachel, Richard Styles und Jared Lopes. Ship-induced waves at Tybee Island, Georgia. Engineer Research and Development Center (U.S.), Dezember 2022. http://dx.doi.org/10.21079/11681/46140.
Der volle Inhalt der QuelleHossain, Niamat Ullah Ibne, Raed Jaradat, Seyedmohsen Hosseini, Mohammad Marufuzzaman und Randy Buchanan. A framework for modeling and assessing system resilience using a Bayesian network : a case study of an interdependent electrical infrastructure systems. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40299.
Der volle Inhalt der QuelleKanninen, M. F. L51718 Development and Validation of a Ductile Fracture Analysis Model. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), Mai 1994. http://dx.doi.org/10.55274/r0010321.
Der volle Inhalt der QuelleHart, Carl, Gregory Lyons und Michael White. Spherical shock waveform reconstruction by heterodyne interferometry. Engineer Research and Development Center (U.S.), Mai 2024. http://dx.doi.org/10.21079/11681/48471.
Der volle Inhalt der QuelleRahmani, Mehran, und Manan Naik. Structural Identification and Damage Detection in Bridges using Wave Method and Uniform Shear Beam Models: A Feasibility Study. Mineta Transportation Institute, Februar 2021. http://dx.doi.org/10.31979/mti.2021.1934.
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