2003-2022年我国竖井施工事故发生规律与分布特征
Patterns and Distribution Characteristics of Shaft Construction Accidents in China(2003-2022)
张军伟 1杨哲 1毛锦波 2张斌斌2
作者信息
- 1. 西南石油大学地球科学与技术学院,成都 610500
- 2. 中交二公局东萌工程有限公司,西安 710119
- 折叠
摘要
为了解我国竖井施工事故发展规律,运用折线图、柱状图、区域分布图、饼状图等图表对2003-2022年间发生的106起竖井事故进行统计分析,结合事故次数、死亡人数与受伤人数指标,获得事故的时间分布特征、空间分布特征及事故等级与类型分布特征,并针对性地提出竖井施工安全防控措施.通过年份、月份、工作日及时间区段分布研究竖井事故的时间分布特征,发现2003-2022年期间3项指标变化具有明显的周期性,事故高发月份是6月,事故高发工作日是周四.对竖井事故空间分布情况进行可视化分析,发现事故高发省份主要位于中部地区,其次是西北以及西南地区.利用死亡人数对竖井事故等级进行判定,发现事故等级以一般事故为主,而较大和重大事故所造成的死亡人数占总体人数的84%左右.将竖井事故类型划分为9种,发现人员坠落、高空坠物、井壁坍塌是最常见的事故类型.
Abstract
To investigate the development patterns of shaft construction accidents in China,this study statistically analyzed 106 shaft construction accidents that occurred from 2003 to 2022.Using line charts,bar charts,regional distribution maps,and pie charts,the study examined accident numbers,fatalities,and injuries to identify temporal distribution characteristics,spatial distribution features,and the classification characteristics of accidents by severity and type.Safety prevention and control measures for shaft construction were proposed based on the findings.Temporal distribution characteristics were analyzed by year,month,weekday,and time period,revealing distinct periodicity in the three indicators during the study period.June was identified as the peak month for accidents,with Thursdays being the most accident-prone weekday.Spatial distribution analysis showed that accidents predominantly occurred in central regions of China,followed by the northwest and southwest regions.Based on fatality counts,the severity of accidents was assessed,revealing that most accidents were categorized as minor,while fatalities from major and sig-nificant accidents accounted for approximately 84%of the total.The study classified shaft construction accidents into nine types,with the most common being personnel falling,falling objects,and shaft wall collapses.
关键词
竖井施工/事故类型/统计分析/分布规律/灾害防控Key words
Shaft construction/Accident types/Statistical analysis/Distribution patterns/Disaster prevention and control引用本文复制引用
出版年
2024
NETL
NSTL
万方数据