Digital twin system for early warning of regional intrusion behavior of personnel in deep foundation pit projects

doi:10.16265/j.cnki.issn1003-3033.2025.08.1471

Abstract

Abstract:

To address the dynamic risks in hazardous areas and the lack of timely warning of personnel intrusion within deep foundation pit, a digital twin system integrating BIM and UWB technology was developed. Initially, a virtual scene representing the deep foundation pit was constructed using the OurBIM 3D graphics engine. Second, a method for calibrating hazardous areas was proposed, and UWB technology was utilized to locate construction personnel and hazardous sources in real-time. Third, a real-time warning mechanism for personnel intrusion behavior was established, and a digital twin system was designed for regional intrusion warning of personnel in deep foundation pit, incorporating functionalities such as scene visualization, hazardous area definition, and warning data logging. Finally, the system was applied to a specific deep foundation pit in Wuhan. The results demonstrate that the system is capable of monitoring personnel intrusion behavior in two distinct types of hazardous areas within the deep foundation pit, achieving a comprehensive warning success rate of 97.4%. This system validates the feasibility and effectiveness of digital twin technology in the construction safety domain.

Key words: deep foundation pit, intrusion behavior, digital twins, hazardous areas, building information modeling (BIM), ultra wide band (UWB)

CLC Number:

X924.3安全监控系统

GONG Peisong, SHEN Xinyun, HAN Bowen, GUO Shengyu, PENG Guanqing, ZHONG Junhao. Digital twin system for early warning of regional intrusion behavior of personnel in deep foundation pit projects[J]. China Safety Science Journal, 2025, 35(8): 33-39.

Figures/Tables 6

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

References 17

[1]	张琦, 王月峰, 李建春, 等. 深基坑紧邻地铁隧道安全评价方法[J]. 中国安全科学学报, 2021, 31(4): 95-104. doi: 10.16265/j.cnki.issn1003-3033.2021.04.013
	ZHANG Qi, WANG Yuefeng, LI Jianchun, et al. Evaluation method for safety of subway tunnels adjacent to deep foundation pits[J]. China Safety Science Journal, 2021, 31(4): 95-104. doi: 10.16265/j.cnki.issn1003-3033.2021.04.013
[2]	TIAN Shuicheng, ZHANG Xinyue, YANG Pengfei, et al. Study on the accident-causing of foundation pit engineering[J]. IOP Conference Series: Earth and Environmental Science, 2018, 156(6): DOI: 10.1088/1755-1315/153/6/062012.
[3]	NIE Shumin, REN Feng. Study on risk management of deep foundation pit engineering[J]. Applied Mechanics and Materials, 2014, 638/639/640: 574-579.
[4]	MEI Xinyu, ZHOU Xiaojing, XU Feng, et al. Human intrusion detection in static hazardous areas at construction sites: deep learning-based method[J]. Journal of Construction Engineering and Management, 2023, 149(1): DOI: 10.1061/(ASCE)CO.1943-7862.0002409.
[5]	KANAN R, ELHASSAN O, BENSALEM R. An IoT-based autonomous system for workers' safety in construction sites with real-time alarming, monitoring, and positioning strategies[J]. Automation in Construction, 2018, 88: 73-86.
[6]	LEE U K, KIM J H, CHO H, et al. Development of a mobile safety monitoring system for construction sites[J]. Automation in Construction, 2009, 18(3): 258-264.
[7]	孙殿阁. 基于数字孪生技术的民用机场安全管理系统构建[J]. 中国安全科学学报, 2023, 33(增1): 222-227.
	SUN Dian'ge. Research on the construction of civil airport safety management system based on digital twin technology[J]. China Safety Science Journal, 2023, 33(S1): 222-227. doi: 10.16265/j.cnki.issn1003-3033.2023.S1.0257
[8]	JIANG Feng, MA Ling, BROYD T, et al. Digital twin and its implementations in the civil engineering sector[J]. Automation in Construction, 2021, 130: DOI: 10.1016/j.autcon.2021.103838.
[9]	OPOKU D G J, PERERA S, OSEI-KYEI R, et al. Digital twin application in the construction industry: a literature review[J]. Journal of Building Engineering, 2021, 40: DOI: 10.1016/j.jobe.2021.102726.
[10]	MADUBUIKE O C, ANUMBA C J, KHALLAF R. A review of digital twin applications in construction[J]. Journal of Information Technology in Construction, 2022, 27: 145-172.
[11]	TUHAISE V V, TAH J H M, ABANDA F H. Technologies for digital twin applications in construction[J]. Automation in Construction, 2023, 152: DOI: 10.1016/j.autcon.2023.104931.
[12]	VENTURA S M, BELLAGENTE P, RINALDI S, et al. Enhancing safety on construction sites: a UWB-based proximity warning system ensuring GDPR compliance to prevent collision hazards[J]. Sensors, 2023, 23(24): DOI: 10.3390/s23249770.
[13]	LI Chunting, CHENG J C P, CHEN Keyu. Top 10 technologies for indoor positioning on construction sites[J]. Automation in Construction, 2020, 118: DOI: 10.1016/j.autcon.2020.103309.
[14]	WU Fengbo, LIU Zhaoqi. Research on UWB / IMU fusion positioning technology in mine[C]. 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS),2020: 934-937.
[15]	CHAN Y T, Hang H Y C, CHING P C. Exact and approximate maximum likelihood localization algorithms[J]. IEEE Transactions on Vehicular Technology, 2006, 55(1): 10-16.
[16]	GRIEVES M, VICKERS J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems[M]. Cham: Springer International Publishing, 2017: 85-113.
[17]	TAO Fei, CHENG Jiangfeng, QI Qinglin, et al. Digital twin-driven product design, manufacturing and service with big data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9/10/11/12): 3563-3576.

定位技术	定位精度	穿透性	抗干扰
UWB	亚厘米-亚毫米	好	强
蓝牙	数米;<8 m	好	较弱
RFID	亚米-亚厘米	差	弱
WIFI	数米;<10 m	较差	较强
超声波	亚米-亚毫米	差	强