School of Business Administration, Liaoning Technical University, Xingcheng City, Liaoning Province, China.
PLoS One. 2024 Oct 17;19(10):e0303996. doi: 10.1371/journal.pone.0303996. eCollection 2024.
With the arrival of Industry 4.0, intelligent construction sites have seen significant development in China. However, accidents involving digitized tower cranes equipped with smart systems continue to occur frequently. Among the main causes of these accidents is human unsafe behavior. To assess the human factors reliability of intelligent construction site tower cranes, it is necessary to shift the safety focus to the human-machine interface and identify patterns of human error behaviors among tower crane drivers through text mining techniques (TF-IDF-TruncatedSVD-ComplementNB). Based on the SHEL model, the behavioral factors influencing human factors reliability in the human-machine interface are categorized and a Performance Shaping Factors (PSF) system is constructed. Building on the foundation of constructing an indicator system for human factors error influence in the driver interface of intelligent construction site tower cranes, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the Interpretive Structural Modeling (ISM) to analyze the importance of various factors in causing human errors and to analyze the logical structure among these factors. Simultaneously, a Bayesian network is constructed using a multi-level hierarchical structural model, thus establishing a new evaluation method for the human-machine interface. The effectiveness of the proposed method is validated through Bayesian network causal inference based on real case studies. The results demonstrate that the evaluation process of this method aligns with the operational scenarios of tower crane drivers in intelligent construction sites. It not only allows for quantifying the likelihood of human errors but also enables the development of targeted measures for controlling unsafe behaviors among tower crane drivers in intelligent construction sites.
随着工业 4.0 的到来，中国的智能建筑工地得到了显著发展。然而，配备智能系统的数字化塔吊事故仍频繁发生。这些事故的主要原因之一是人为不安全行为。为了评估智能建筑工地塔吊的人为因素可靠性，有必要将安全重点转移到人机界面，并通过文本挖掘技术（TF-IDF-TruncatedSVD-ComplementNB）识别塔吊司机的人为错误行为模式。基于 SHEL 模型，对人机界面中影响人为因素可靠性的行为因素进行分类，并构建性能塑造因素（PSF）系统。在构建智能建筑工地塔吊驾驶员界面人为因素误差影响指标体系的基础上，结合决策试验和评价实验室（DEMATEL）方法和解释结构建模（ISM）分析人为误差产生中各种因素的重要性，并分析这些因素之间的逻辑结构。同时，使用多层次层次结构模型构建贝叶斯网络，从而建立人机界面的新评价方法。通过基于真实案例研究的贝叶斯网络因果推理验证了所提出方法的有效性。结果表明，该方法的评估过程符合智能建筑工地塔吊司机的操作场景。它不仅可以量化人为错误的可能性，还可以针对智能建筑工地塔吊司机的不安全行为制定有针对性的控制措施。
