EAGER: Uncertainty Quantification of Structural Systems: Generalized Information Theory

EAGER：结构系统的不确定性量化：广义信息论

• 批准号: 1552855
• 负责人: Ross Corotis
• 金额: $ 15.52万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552855&HistoricalAwards=false
• 关键词: EAGER; Uncertainty; Quantification; Structural; Systems

Uncertainty is inherent in the assessment and prediction of the performance of structures and community infrastructure when evaluating risk, hazard mitigation and vulnerability/resiliency. Structural engineering needs to reflect multi-disciplinary societal issues, including social, political and economic, which introduce partial knowledge and disparate characteristics of uncertainty. While traditional probabilistic methods to treat the uncertainty can address partial and dissimilar information, they are not the most appropriate for comprehensive incorporation of broader contexts reflecting sources such as expert judgement and imprecision. This EArly-concept Grant for Exploratory Research (EAGER) project undertakes exploratory research on the broader aspects of uncertainty approaches to structural engineering safety, incorporating multi-attribute aspects of judgement and unpredictability in human decision-making. The research introduces varied sources of uncertainty into structural design, melding this with community issues that assess the value of the structure to economic, environmental and social/political sustainability. Quantitative and linguistic uncertainties are incorporated under a comprehensive design philosophy capable of handling vagueness, and ambiguity. These broadened approaches to structural engineering and community performance will lead to great benefits to society through enhancements of the built environment. The primary objective of this project is the development of methods under the umbrella of Generalized Information Theory that expand traditional probabilistic approaches to structural and community safety, enhancing the treatment of uncertainty by incorporating notions of ignorance, interactivity, and linguistic information. The scope of the project includes generalization of probability theory with uncertainty measures based on monotone theory and incorporating linguistic vagueness, ambiguity, evidence concepts (belief and plausibility), conflicting expert judgement, imprecision, partial knowledge, confidence, and subjective possibility. An initial application in this exploratory project will be for earthquake damage estimation and vulnerability assessment. With actual community data, fuzzy pattern recognition will be used to investigate the existence of building damage patterns and potential seismic vulnerability prediction.

在评估风险、减灾和脆弱性/复原力时，结构和社区基础设施性能的评估和预测固有的不确定性。结构工程需要反映多学科的社会问题，包括社会，政治和经济，这引入了部分知识和不确定性的不同特征。虽然处理不确定性的传统概率方法可以处理部分和不同的信息，但它们并不适合全面纳入反映专家判断和不精确性等来源的更广泛背景。EARLY概念探索性研究（EAGER）项目对结构工程安全的不确定性方法的更广泛方面进行探索性研究，将人类决策中的判断和不可预测性的多属性方面结合起来。该研究在结构设计中引入了各种不确定性来源，并将其与评估结构对经济，环境和社会/政治可持续性价值的社区问题相融合。定量和语言的不确定性被纳入一个全面的设计理念，能够处理的简洁性，和模糊性。这些拓宽的结构工程和社区绩效方法将通过改善建筑环境为社会带来巨大利益。该项目的主要目标是广义信息理论的保护伞下的方法，扩展传统的概率方法结构和社区安全，提高治疗的不确定性，将无知，互动和语言信息的概念。该项目的范围包括概率理论的推广与不确定性措施的基础上单调理论，并纳入语言的准确性，模糊性，证据概念（信念和可信度），冲突的专家判断，不精确性，部分知识，信心和主观的可能性。这一探索性项目的初步应用将是地震损害估计和脆弱性评估。利用实际社区数据，模糊模式识别将用于调查建筑物损坏模式的存在和潜在的地震易损性预测。