Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

被引:12
|
作者
Duan, Pinsheng [1 ]
Zhou, Jianliang [1 ]
Goh, Yang Miang [2 ]
机构
[1] China Univ Min & Technol, Sch Mech & Civil Engn, Xuzhou 221116, Jiangsu, Peoples R China
[2] Natl Univ Singapore, Coll Design & Engn, Dept Built Environm, 4 Architecture Dr, Singapore 117566, Singapore
基金
中国国家自然科学基金;
关键词
Construction worker; Movement trajectory; Safety risk; Spatial-temporal pattern; Complex network; MISS INTERACTIONS; STRUCK-BY; ON-FOOT; HAZARD; MODEL; ACCIDENTS; PATTERNS; SYSTEMS;
D O I
10.1016/j.aei.2023.101990
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial-temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spa-tial-temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group tra-jectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spa-tial-temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for con-struction workers.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Spatial-temporal Interrelationships of Safety Risks with Dynamic Partition Analysis: A Mechanical Installation Case
    Liao, Pin-Chao
    Guo, Zhonghua
    Tsai, Chung-Han
    Ding, Jiawei
    KSCE JOURNAL OF CIVIL ENGINEERING, 2018, 22 (05) : 1572 - 1583
  • [2] Causal Analysis and Prevention Strategies for Safety Risks in Prefabricated Building Construction Based on Accident Data and Complex Network Theory
    Liu, Wei
    Luo, Xiao
    Liang, Baojun
    Xie, Junhao
    BUILDINGS, 2025, 15 (02)
  • [3] Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System
    Chen, Hao
    Mao, Yingchi
    Wang, Longbao
    Qi, Hai
    SENSORS, 2020, 20 (09)
  • [4] Understanding structure of urban traffic network based on spatial-temporal correlation analysis
    Yang, Yanfang
    Jia, Limin
    Qin, Yong
    Han, Shixiu
    Dong, Honghui
    MODERN PHYSICS LETTERS B, 2017, 31 (22):
  • [5] Understanding Safety Performance of Prefabricated Construction Based on Complex Network Theory
    Song, Liangliang
    Li, Haiyan
    Deng, Yongliang
    Li, Chaozhi
    APPLIED SCIENCES-BASEL, 2022, 12 (09):
  • [6] Spatial-Temporal Analysis of Vehicle Routing Problem from Online Car-Hailing Trajectories
    Feng, Xuyu
    Yu, Jianhua
    Kan, Zihan
    Zhou, Lin
    Tang, Luliang
    Yang, Xue
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 12 (08)
  • [7] Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data
    Zhang, Hui
    Liu, Yanjun
    Shi, Baiying
    Jia, Jianmin
    Wang, Wei
    Zhao, Xiang
    JOURNAL OF ADVANCED TRANSPORTATION, 2021, 2021
  • [8] Spatial-Temporal Sub-Pixel Mapping Based on Swarm Intelligence Theory
    He, Da
    Zhong, Yanfei
    Feng, Ruyi
    Zhang, Liangpei
    REMOTE SENSING, 2016, 8 (11)
  • [9] Temporal Characteristics and Spatial Homogeneity of Virtual Water Trade: A Complex Network Analysis
    Fan, Xinghua
    Li, Xuxia
    Yin, Jiuli
    Liang, Jiaochen
    WATER RESOURCES MANAGEMENT, 2019, 33 (04) : 1467 - 1480
  • [10] An effective spatial-temporal attention based neural network for traffic flow prediction
    Do, Loan N. N.
    Vu, Hai L.
    Vo, Bao Q.
    Liu, Zhiyuan
    Dinh Phung
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 108 : 12 - 28