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年美国复苏和再投资法案（公法111-5）资助的。该早期职业发展（CAREER）项目的研究目标是开发一个合作人机模型更新认知系统（MUCogS）的框架。 常用的数值模拟技术，如有限元或边界元，提供简单结构的精确表示，但直到数据和额外的观察纳入这些模型，他们往往无法表示复杂的现有结构的物理参数与理想的精度水平。 模型更新技术被用来增强这些数值模型的基础上的实验数据。 当前的模型更新技术通过最小化实验测量与数值模型产生的数据之间的误差来获得最佳匹配现有结构的模型。 分析师仅限于选择一个初始模型，调整一些算法参数或？旋钮？并且对由算法本身执行的过程几乎没有控制。 本研究通过设计先进的算法来检测模型更新问题的几个合理的解决方案来改变这种范式。 经过培训的工程师可以使用他/她的工程判断来选择一个或多个适当的模型用于后续分析。 通过这个框架，MUCogS正式包括模型更新技术的分析师，创建一个合作的人机系统。这项研究在结构工程的任何领域都有直接的应用，包括现有结构的数值建模（例如，地震工程、风力工程和结构健康监测）。 这项研究的成功完成将大大提高工程师？的能力，以验证创新的设计，评估性能和检测现有结构的损坏。 这一进步直接影响到州、联邦和私人用于基础设施维护和更换的预算。 本研究的教育部分包括通过在结构工程课程中实施探究式教学，培养批判性思维，从而发展本科生的工程判断力。 外联活动的重点是让少数民族学生人数众多的城市学校接触结构工程。此外，本科生研究助理包括在项目期间的研究活动。