PDF(Pubmed)
Abstract：
Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.
摘要：
睡眠不足是影响许多行业工人的关键问题，包括建筑。它对工人产生不利影响，并可能导致对他们健康的重大担忧，安全,和整体工作表现。一些研究调查了睡眠剥夺对安全性和生产力的影响。尽管已经研究了睡眠剥夺通过认知障碍对安全性和生产力的影响，关于睡眠不足与导致工作场所危害和伤害的影响因素的相关性的研究仍然有限.为了填补文献中的这一空白，这项研究利用机器学习算法来预测危险情况。此外，这项研究证明了机器学习算法的适用性，包括支持向量机和随机森林，通过根据240名建筑工人的反应预测建筑工人的睡眠不足，确定七个主要指标作为预测因子。结果表明，支持向量机算法在验证过程中产生了优越的睡眠剥夺预测结果。研究结果为建筑业的利益相关者提供了显着的好处，特别是项目和安全经理。通过实施有针对性的干预措施，这些见解可以通过及时准确地预测睡眠剥夺来帮助减少事故并提高工作场所的安全性。
