Modeling and Control of Human Errors Using Expert Systems

使用专家系统对人为错误进行建模和控制

• 批准号: 8715496
• 负责人: Andrzej Nowak
• 金额: $ 17.32万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-08-15 至 1991-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8715496&HistoricalAwards=false
• 关键词: Modeling; Control; Human; Errors; Using

Human error is the major cause of structural failure. Therefore, modeling and control of errors are an important part of the strategy to improve the reliability of structures. The objective of this study is to develop a practical method to predict the likelihood of occurrence of human error in structural engineering. This involves estimating the sensitivity of the structure to the hazard of gross errors in design, construction and use. Human errors are influenced by a number of conditions which constitute the human, economical, social and political climate influencing each individual project. These climatic conditions and their relations to errors constitutes the data of a knowledge-based expert system (KBES). A level of risk is associated with this measure of sensitivity. Different levels of risk and structural context call for different strategies for risk management. Another objective is to develop a risk management procedure in the form of a contingency plan to mitigate the risk entailed by the gross error sensitivity. Sensitivity analysis is an effective tool extended to control some of the predictable errors. Sensitivity analysis would be extended to include a proper cost- benefit analysis of allocation options. The work is based on the results of a previous study (NSF grant MSM-8304962) and the recommendations of NSF Workshop on Modeling Human Errors (June, 1986).

人为错误是结构失效的主要原因。 因此，误差建模和控制是提高结构可靠性策略的重要组成部分。 本研究的目的是开发一种实用方法来预测结构工程中人为错误发生的可能性。 这涉及估计结构对设计、施工和使用中严重错误的危害的敏感性。 人为错误受到多种条件的影响，这些条件构成了影响每个项目的人类、经济、社会和政治气候。 这些气候条件及其与误差的关系构成了基于知识的专家系统（KBES）的数据。 风险水平与这种敏感性度量相关。 不同级别的风险和结构背景需要不同的风险管理策略。 另一个目标是以应急计划的形式制定风险管理程序，以减轻严重错误敏感性带来的风险。 敏感性分析是一种有效的工具，可以扩展到控制一些可预测的错误。 敏感性分析将扩展到包括对分配方案进行适当的成本效益分析。 这项工作基于之前的研究结果（NSF 拨款 MSM-8304962）和 NSF 人为错误建模研讨会（1986 年 6 月）的建议。