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

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。这一早期职业发展(CALEAR)项目的研究目标是开发一个合作的人机模型更新认知系统(MUCogs)的框架。常用的数值建模技术，如有限元或边界元，可以提供简单结构的精确表示，但在将数据和其他观测数据纳入这些模型之前，它们往往无法以理想的精度表示复杂现有结构的物理参数。利用模型修正技术对这些基于实验数据的数值模型进行了改进。当前的模型更新技术通过最小化实验测量和由数值模型产生的数据之间的误差来获得与现有结构最匹配的模型。分析人员仅限于选择初始模型，调整少量的算法参数或旋钮。并且对算法本身执行的过程几乎没有控制。这项研究通过设计先进的算法来检测模型更新问题的几个看似合理的解决方案，从而改变了这种范式。训练有素的工程师可以利用他/她的工程判断来选择一个或多个适当的模型进行后续分析。通过这个框架，MUCogS正式包含了分析人员对模型更新技术的研究，创建了一个协作式的人机系统。这项研究可以直接应用于任何结构工程领域，包括现有结构的数值模拟(如地震工程、风工程和结构健康监测)。这项研究的成功完成将显著提高S工程师验证创新设计、评估性能和检测现有结构损伤的能力。这一进展直接影响到用于基础设施维护和更换的州、联邦和私人预算。本研究的教育内容包括通过在结构工程课堂上实施探究式教学，通过培养学生的批判性思维来培养大学生的工程判断能力。外展活动的重点是让拥有大量少数民族学生的城市学校接触结构工程。此外，项目期间的研究活动还包括本科生研究助理。

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.