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基于逻辑回归模型的露天煤矿非致命事故分析——案例研究

Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study.

作者信息

Onder Seyhan, Mutlu Mert

机构信息

a Department of Mining Engineering , Eskişehir Osmangazi University , Eskisehir , Turkey.

b Department of Mining Engineering , Aksaray University , Aksaray , Turkey.

出版信息

Int J Inj Contr Saf Promot. 2017 Sep;24(3):328-337. doi: 10.1080/17457300.2016.1178299. Epub 2016 May 11.

Abstract

Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

摘要

事故对采矿业的工人和企业都会造成重大损失。为减少职业事故的数量,应妥善记录并仔细分析这些事故。本研究对2006年至2011年间土耳其煤炭企业(TKI)位于索马的爱琴海褐煤企业(ELI)进行了有效考察,并利用露天煤矿职业事故记录进行统计分析。本研究共分析了231起职业事故。事故记录分为七组:区域、原因、职业、身体部位、年龄、轮班时间和误工天数。本研究使用SPSS软件包程序进行逻辑回归分析,预测非致命伤害导致误工天数大于或小于3天的事故概率。社会设施——地面设施、车间和露天矿区所在区域是导致非致命伤害误工天数大于3天的事故发生概率最高的区域,而此类事故发生概率最高的原因是运输和人工搬运。此外,该模型还针对2012年索马ELI发生的此类报告事故进行了测试,并正确估计了误工事故暴露概率,准确率达70%。

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