大型工程安全隐患管理协作特征挖掘

张东成, 强茂山, 江汉臣, 黄钰洁

清华大学学报(自然科学版) ›› 2022, Vol. 62 ›› Issue (2) : 208-214.

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PDF(7860 KB)
清华大学学报(自然科学版) ›› 2022, Vol. 62 ›› Issue (2) : 208-214. DOI: 10.16511/j.cnki.qhdxxb.2021.22.045
专题:建设管理

大型工程安全隐患管理协作特征挖掘

  • 张东成1, 强茂山1, 江汉臣2, 黄钰洁1
作者信息 +

Mining safety hazard management collaboration features from large construction projects

  • ZHANG Dongcheng1, QIANG Maoshan1, JIANG Hanchen2, HUANG Yujie1
Author information +
文章历史 +

摘要

大型工程建设的安全隐患管理对保障施工安全具有重要意义。该文基于某大型工程信息系统3年多记录的安全隐患管理协作数据,采用文本挖掘技术,包括自动分词、词性标注和词频-逆向文件频率(TF-IDF)算法等,提出一套隐患特征提取和分类方法,可用于隐患特征的动态分析,以实时掌握工程现场的安全隐患管理状态。基于该特征挖掘方法,分别进行了隐患特征与隐患类型、单位角色的交互分析,并进一步探索隐患特征对整改效率的影响。该研究为工程现场的安全隐患管理提供了方法的支撑,有助于抓住隐患管理重点,提升管理效率。

Abstract

Safety hazard management of large construction projects is essential to ensuring construction safety. This study used text mining of safety hazard management information for more than three years collected from a large construction project's information system with automatic word segmentation, part-of-speech tagging and term frequency (TF)-inverse document frequency (IDF) analyses to extract and classify hazard features. This methodology analyzes the safety hazard features to identify the safety hazard conditions on site in real time. The hazard features are further analyzed by different hazard types and different roles of the organizations. This study also investigates the impact of the hazard features on the management efficiency. This paper provides an effective on site safety hazard management method which can help managers focus on the key needs and improve construction efficiency.

关键词

大型工程建设 / 安全隐患 / 管理协作 / 特征挖掘

Key words

large construction projects / safety hazards / management collaboration / feature mining

引用本文

导出引用
张东成, 强茂山, 江汉臣, 黄钰洁. 大型工程安全隐患管理协作特征挖掘[J]. 清华大学学报(自然科学版). 2022, 62(2): 208-214 https://doi.org/10.16511/j.cnki.qhdxxb.2021.22.045
ZHANG Dongcheng, QIANG Maoshan, JIANG Hanchen, HUANG Yujie. Mining safety hazard management collaboration features from large construction projects[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(2): 208-214 https://doi.org/10.16511/j.cnki.qhdxxb.2021.22.045

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基金

国家自然科学基金资助项目(51779124)

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