1. Institute of Project Management and Construction Technology, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China; 2. School of Public Policy and Management, Tsinghua University, Beijing 100084, China
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.
[1] ZHOU Z P, GOH Y M, LI Q M. Overview and analysis of safety management studies in the construction industry[J]. Safety Science, 2015, 72:337-350. [2] LEUNG M Y, CHAN Y S, YUEN K W. Impacts of stressors and stress on the injury incidents of construction workers in Hong Kong[J]. Journal of Construction Engineering and Management, 2010, 136(10):1093-1103. [3] ZHOU Q, FANG D P, MOHAMED S. Safety climate improvement:Case study in a Chinese construction company[J]. Journal of Construction Engineering and Management, 2011, 137(1):86-95. [4] JIANG Z M, FANG D P, ZHANG M Z. Experiment-based simulations of construction worker safety behavior[J]. Journal of Tsinghua University (Science and Technology), 2014, 54(10):1327-1332. (in Chinese)蒋中铭, 方东平, 张铭宗. 基于实验设计的建筑工人安全相关行为仿真[J]. 清华大学学报(自然科学版), 2014, 54(10):1327-1332. [5] XU S S, WANG X Q, XU Z C. Governance of core safety risks of urban underground engineering:The formation and evolution of multi-party collaborative strategies[J]. China Civil Engineering Journal, 2017, 50(9):90-103. (in Chinese)许树生, 王雪青, 徐志超. 城市地下工程核心安全风险治理:多方协同策略的形成与演化[J]. 土木工程学报, 2017, 50(9):90-103. [6] TEIZER J, ALLREAD B S, FULLERTON C E, et al. Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system[J]. Automation in Construction, 2010, 19(5):630-640. [7] LEE H S, LEE K P, PARK M, et al. RFID-based real-time locating system for construction safety management[J]. Journal of Computing in Civil Engineering, 2012, 26(3):366-377. [8] CARBONARI A, GIRETTI A, NATICCHIA B. A proactive system for real-time safety management in construction sites[J]. Automation in Construction, 2011, 20(6):686-698. [9] ZHANG M Y, CAO T Z, ZHAO X F. Applying sensor-based technology to improve construction safety management[J]. Sensors, 2017, 17(8):1841. [10] WU W W, YANG H J, CHEW D A S, et al. Towards an autonomous real-time tracking system of near-miss accidents on construction sites[J]. Automation in Construction, 2010, 19(2):134-141. [11] PARK C S, KIM H J. A framework for construction safety management and visualization system[J]. Automation in Construction, 2013, 33:95-103. [12] ZHANG S J, SULANKIVI K, KIVINIEMI M, et al. BIM-based fall hazard identification and prevention in construction safety planning[J]. Safety Science, 2015, 72:31-45. [13] LEI Z, TANG W Z, DUFFIELD C F, et al. Qualitative analysis of the occupational health and safety performance of Chinese international construction projects[J]. Sustainability, 2018, 10(12):4344. [14] GUO S Y, LUO H B, YONG L. A big data-based workers behavior observation in China metro construction[J]. Procedia Engineering, 2015, 123:190-197. [15] LIN P, WEI P C, FAN Q X, et al. CNN model for mining safety hazard data from a construction site[J]. Journal of Tsinghua University (Science and Technology), 2019, 59(8):628-634. (in Chinese)林鹏, 魏鹏程, 樊启祥, 等. 基于CNN模型的施工现场典型安全隐患数据学习[J]. 清华大学学报(自然科学版), 2019, 59(8):628-634. [16] LI X D, FEI Y F, RIZZUTO T E, et al. What are the occupational hazards of construction project managers:A data mining analysis in China[J]. Safety Science, 2021, 134:105088. [17] AMIRI M, ARDESHIR A, FAZEL ZARANDI M H, et al. Pattern extraction for high-risk accidents in the construction industry:A data-mining approach[J]. International Journal of Injury Control and Safety Promotion, 2016, 23(3):264-276. [18] SUN J. Jieba Chinese word segmentation tool[Z]. 2012. [19] GUAN Q, DENG S H, WANG H. Chinese stopwords for text clustering:A comparative study[J]. Data Analysis and Knowledge Discovery, 2017, 1(3):72-80. (in Chinese)官琴, 邓三鸿, 王昊. 中文文本聚类常用停用词表对比研究[J]. 数据分析与知识发现, 2017, 1(3):72-80. [20] ABDELHAMID T S, EVERETT J G. Identifying root causes of construction accidents[J]. Journal of Construction Engineering and Management, 2000, 126(1):52-60. [21] PERLMAN A, SACKS R, BARAK R. Hazard recognition and risk perception in construction[J]. Safety Science, 2014, 64:22-31. [22] BAI Y, JIA R X. Elite recruitment and political stability:The impact of the abolition of China's civil service exam[J]. Econometrica, 2016, 84(2):677-733. [23] LI H, LU M J, HSU S C, et al. Proactive behavior-based safety management for construction safety improvement[J]. Safety Science, 2015, 75:107-117.
2022, Vol. 62 
