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Titlebook: 20th International Probabilistic Workshop; IPW 2024 José C. Matos,Paulo B. Louren?o,Hélder S. Sousa Conference proceedings 2024 The Editor(

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期刊全稱(chēng)20th International Probabilistic Workshop
期刊簡(jiǎn)稱(chēng)IPW 2024
影響因子2023José C. Matos,Paulo B. Louren?o,Hélder S. Sousa
視頻videohttp://file.papertrans.cn/101/100496/100496.mp4
發(fā)行地址Contains latest research developments in probability and statistics for engineering applications.Provides methods and tools for uncertainty quantification and modeling.Illustrates the application of r
學(xué)科分類(lèi)Lecture Notes in Civil Engineering
圖書(shū)封面Titlebook: 20th International Probabilistic Workshop; IPW 2024 José C. Matos,Paulo B. Louren?o,Hélder S. Sousa Conference proceedings 2024 The Editor(
影響因子This volume presents the proceedings of the 20th International Probabilistic Workshop (IPW), which was held in Guimar?es, Portugal on May 8-10, 2024. Probabilistic methods are currently of crucial importance for research and developments in the field of engineering, which face challenges presented by new materials and technologies and rapidly changing societal needs and values. Contemporary needs related to, for example, performance-based design, service-life design, life-cycle analysis, product optimization, assessment of existing structures and structural robustness give rise to new developments as well as accurate and practically applicable probabilistic and statistical engineering methods to support these developments. These proceedings are a valuable resource for anyone interested in contemporary developments in the field of probabilistic engineering applications..
Pindex Conference proceedings 2024
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Lecture Notes in Civil Engineeringhttp://image.papertrans.cn/012/image/100496.jpg
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20th International Probabilistic Workshop978-3-031-60271-9Series ISSN 2366-2557 Series E-ISSN 2366-2565
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https://doi.org/10.1007/978-3-031-60271-9Risk assessment; Risk management; application of probability theories; decision-making processes; infras
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978-3-031-60273-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-663-09194-3ing the flutter limit. Parametric variations in the depth and width of the fairing shape are introduced as variables in the optimization process. Several methods for prediction, namely Polynomial, Extreme machine Learning, and Gaussian Process, are used to build the response surface model, to ensure
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Michael Th. Greven,Hans-Gerd Schumannthe extent and duration of functionality loss. In simpler terms, the goal is to enhance the resilience. This contribution presents the outcomes of the REMAIN project (REsilient MotorwAy INfrastructure)..Within REMAIN, a framework was developed to assess the resilience of Austria‘s entire motorway in
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