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首页> 外文期刊>Journal of Construction Engineering and Management >Human Error Identification and Analysis for Shield Machine Operation Using an Adapted TRACEr Method
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Human Error Identification and Analysis for Shield Machine Operation Using an Adapted TRACEr Method

机译：一种使用适应跟踪方法屏蔽机操作的人为错误识别与分析

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This paper investigated shield machine operation (SMO) errors involved in shield tunneling construction accidents based on the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). Human errors are classified and identified at a coarse-grained task level in the TRACEr framework, which could cause failures to completely identify and analyze the human errors in a given accident. This motivated us to propose an adapted TRACEr to overcome the limitation. The adapted TRACEr incorporates hierarchical task analysis (HTA) to decompose a task into combinations of activities, which helps describe human errors at a fine-grained activity level. The connection between the added activity level and the cognitive functions was constructed according to the Phoenix method. Based on the adaptation, an activity-oriented structure of human error taxonomies was developed, and a corresponding retrospective analysis procedure that focuses on identifying errors under various construction operational situations was proposed. Based on the adapted TRACEr, SMO errors were identified and analyzed. The error taxonomies of SMO were developed, and 72 accidents were retrospectively analyzed to identify and code the errors. Data mining techniques were applied to analyze the fine-grained SMO error data to reveal the main manifestations of SMO errors and the hidden associated rules for their cognitive failures. Consequently, several targeted cognitive-based human error mitigation strategies were proposed, showing the application potential of the adapted TRACEr as a human error management tool in the construction industry.
机译：本文基于对认知错误的回顾性和预测分析的技术研究了屏蔽机操作（SMO）误差涉及盾构隧道施工事故的误差（跟踪误差分析。人类错误被分类并在追踪框架中的粗粒度任务水平上识别，这可能导致故障完全识别和分析人类错误在给定的事故中。这使我们提出了一种适应的示踪剂来克服限制。适应的示踪剂包括分层任务分析（HTA），以将任务分解为活动的组合，这有助于在细粒度的活动水平下描述人类误差。根据Phoenix方法构建了增加的活动水平与认知功能之间的连接。基于适应性，开发了一种面向活动的人体误差分类结构，提出了相应的回顾性分析程序，专注于在各种施工情况下识别误差。基于适应的示踪剂，鉴定并分析了SMO误差。开发了SMO的错误分类，并回顾性分析了72条事故以识别和编写错误。应用数据挖掘技术来分析细粒度的SMO误差数据，以揭示SMO误差和隐藏相关规则的主要表现为他们的认知失败。因此，提出了几种基于目标的基于认知的人体错误缓解策略，显示了适用的示踪剂作为建筑行业人为错误管理工具的应用潜力。

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