Analysis of interrelationships of human errors using linguistic decision-making trial and evaluation laboratory with consensus reaching process

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Qiaohong Zheng , Xinwang Liu
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引用次数: 0

Abstract

Analyzing human errors' interrelationships is one of the most important assignments for human reliability improvement in sociotechnical systems. Human factor analysis and classification system (HFACS) is effective in human error analysis due to its taxonomy and systematical perspective. It reveals interrelationships among human errors emerging in a multi-hierarchy of systems. However, the conventional HFACS method is incapable of quantifying their interrelationship. Especially, due to the nature of human errors, their objective data is limited. Experts' opinions are important resources to facilitate human error analysis. However, limited improved HFACS considers experts' consensus on interrelationships analysis results, especially in linguistic environments. Accordingly, this paper aims to address HFACS-based interrelationships analysis problems utilizing linguistic decision-making trial and evaluation laboratory (DEMATEL) with consensus reaching process (CRP). First, probabilistic linguistic terms are utilized to represent experts' opinions on human errors' interrelationships. Second, CRP is introduced to derive consensual opinions on human errors' interrelationships, shifting the focus to identifying human errors with low consensus levels rather than experts. Then, a hybrid weighting method is introduced to determine the weight of experts' opinions in the information fusion phase, which reflects inherent uncertainty and inter-recognition of experts’ opinions. Furthermore, DEMATEL is introduced to model direct and indirect interrelationships among human errors. Finally, a case study of a drug administration process is conducted to validate the efficiency of the proposed method. The case study indicates that neglect of safety culture development and limited financial and human resources are the top two human errors, with importance degree 0.148 and 0.107.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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