CAREER: Cooperative Human-Computer Model Updating Cognitive Systems (MUCogS)

职业：协作人机模型更新认知系统（MUCogS）

• 批准号: 0846258
• 负责人: Juan Caicedo
• 金额: $ 43万
• 依托单位: University South Carolina Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0846258&HistoricalAwards=false
• 关键词: CAREER; Cooperative; Human; Computer; Model

This award is funded under the American recovery and Reinvestment Act of 2009 (Public Law 111-5)The research objective of this early career development (CAREER) project is to develop a framework for cooperative human-computer Model Updating Cognitive systems (MUCogS). Commonly used numerical modeling techniques, such as finite or boundary elements, provide accurate representations of simple structures, but until data and additional observations are incorporated into these models they often fail to represent the physical parameters of complex existing structures with desirable levels of accuracy. Model updating techniques are used to enhance these numerical models based on experimental data. Current model updating techniques obtain one model that best matches the existing structure by minimizing the error between experimental measurements and data produced by the numerical model. The analyst is limited to the selection of an initial model, adjusting few algorithm parameters or ?knobs? and has little control on the process performed by the algorithm itself. This research changes this paradigm by designing advances algorithms to detect several plausible solutions to the model updating problem. A trained engineer can use his/her engineering judgment to select one or several appropriated models for subsequent analysis. Through this framework MUCogS formally include the analyst on the model updating technique creating a cooperative human-computer system.This research has direct application in any field of structural engineering that incorporates numerical modeling of existing structures (e.g., earthquake engineering, wind engineering and structural health monitoring). The successful completion of this research will significantly enhance an engineer?s ability to validate innovative designs, evaluate the performance and detect damage of existing structures. This advancement directly impacts state, federal and private budgets dedicated to infrastructure maintenance and replacement. The educational component of this research includes the development of engineering judgment among undergraduate students by nurturing critical thinking through the implementation of inquiry-based teaching in structural engineering classes. The outreach activities focus on exposing urban schools, with a significant population of minority students, to structural engineering. In addition, undergraduate research assistants are included on the research activities during the duration of the project.

该奖项是根据2009年《美国恢复和再投资法》（公法111-5）资助的，该早期职业发展项目（职业）项目的研究目标是为合作的人类计算机模型更新认知系统（MUCOGS）开发一个框架。 常用的数值建模技术（例如有限或边界元素）提供了简单结构的准确表示，但是直到将数据和其他观察结果纳入这些模型之前，它们通常无法代表具有可取精度水平的复杂现有结构的物理参数。 模型更新技术用于基于实验数据来增强这些数值模型。 当前的模型更新技术获得了一个模型，该模型通过最大程度地减少了实验测量和数值模型产生的数据之间的误差，从而最能匹配现有结构。 分析师仅限于选择初始模型，调整很少的算法参数还是？旋钮？并且几乎无法控制算法本身执行的过程。 这项研究通过设计进步算法来检测模型更新问题的几种合理解决方案来改变此范式。 训练有素的工程师可以使用其工程判断选择一种或几种分配模型进行后续分析。 通过此框架，粘液正式包括模型更新技术的分析师创建合作人类计算机系统的分析师。这项研究已在任何结合了现有结构的数字建模（例如，地震工程，风能工程和结构健康监测）的任何结构工程领域的直接应用。 这项研究的成功完成将显着增强工程师验证创新设计，评估和检测现有结构损害的能力。 这种进步直接影响致力于基础设施维护和替代的州，联邦和私人预算。 这项研究的教育部分包括通过实施基于询问的结构工程课程来培养批判性思维，从而在本科生中发展工程判断。 外展活动的重点是将城市学校（大量少数族裔学生）暴露于结构工程上。此外，在项目期间，研究助理包括本科研究助理。