IoT sensor-based BIM system for smart safety barriers of hazardous energy in petrochemical construction

Lieyun DING, Weiguang JIANG, Cheng ZHOU

PDF(6371 KB)
PDF(6371 KB)
Front. Eng ›› 2022, Vol. 9 ›› Issue (1) : 1-15. DOI: 10.1007/s42524-021-0160-6
RESEARCH ARTICLE
RESEARCH ARTICLE

IoT sensor-based BIM system for smart safety barriers of hazardous energy in petrochemical construction

Author information +
History +

Abstract

The accidental release of hazardous energy is one of the causes of construction site accidents. This risk is considerably increased during petrochemical plant construction because the project itself is complex in terms of process, equipment, and environment. In addition, a general construction safety barrier hardly isolates and controls site hazardous energy effectively. Thus, this study proposes an Internet of Things (IoT) sensor-based building information modeling (BIM) system, which can be regarded as a new smart barrier design method for hazardous energy in petrochemical construction. In this system, BIM is used to support the identification of on-site hazardous energy, whereas IoT is used to collect the location of on-site personnel in real time. A hazardous energy isolation rule is defined to enable the system to generate a smart barrier on the web terminal window, thereby ensuring the safety of on-site person. This system has been applied to a large-scale construction project in Sinopec for one year and accumulated substantial practical data, which supported the idea about the application of sensor and BIM technology in construction. The related effects of the system on hazardous energy management are also presented in this work.

Graphical abstract

Keywords

IoT / BIM / smart safety barrier / hazardous energy management / petrochemical construction

Cite this article

Download citation ▾
Lieyun DING, Weiguang JIANG, Cheng ZHOU. IoT sensor-based BIM system for smart safety barriers of hazardous energy in petrochemical construction. Front. Eng, 2022, 9(1): 1‒15 https://doi.org/10.1007/s42524-021-0160-6

References

[1]
Amir-Heidari P, Maknoon R, Taheri B, Bazyari M (2017). A new framework for HSE performance measurement and monitoring. Safety Science, 100: 157–167
CrossRef Google scholar
[2]
Arslan M, Cruz C, Ginhac D (2019). Visualizing intrusions in dynamic building environments for worker safety. Safety Science, 120: 428–446
CrossRef Google scholar
[3]
Chen H, Hou L, Zhang G K, Moon S (2021). Development of BIM, IoT and AR/VR technologies for fire safety and upskilling. Automation in Construction, 125: 103631
CrossRef Google scholar
[4]
Dave B, Buda A, Nurminen A, Främling K (2018). A framework for integrating BIM and IoT through open standards. Automation in Construction, 95: 35–45
CrossRef Google scholar
[5]
Duijm N J, Andersen H B, Hale A, Goossens L, Hourtolou D (2004). Evaluating and managing safety barriers in major hazard plants. In: 7th International Conference on Probabilistic Safety Assessment and Management. Berlin: Springer, 110–115
CrossRef Google scholar
[6]
Duijm N J, Fiévez C, Gerbec M, Hauptmanns U, Konstandinidou M (2008). Management of health, safety and environment in process industry. Safety Science, 46(6): 908–920
CrossRef Google scholar
[7]
Gibson J J (1961). The contribution of experimental psychology to the formulation of the problem of safety—A brief for basic research. Behavioural Approaches to Accident Research. Association for the Aid of Crippled Children
[8]
Guo S Y, Ding L Y, Luo H B, Jiang X Y (2016). A Big-Data-based platform of workers’ behavior: Observations from the field. Accident Analysis and Prevention, 93: 299–309
CrossRef Pubmed Google scholar
[9]
Hale A, Goossens L, Ale B, Bellamy L, Post J, Oh J, Papazoglou I A (2004). Managing safety barriers and controls at the workplace. In: 7th International Conference on Probabilistic Safety Assessment and Management. Berlin: Springer, 608–614
CrossRef Google scholar
[10]
Hopkins A (2012). Disastrous Decisions: The Human and Organisational Causes of the Gulf of Mexico Blowout. Sydney: CCH Australia Limited
[11]
Hossain M A, Abbott E L, Chua D K, Nguyen T Q, Goh Y M (2018). Design-for-safety knowledge library for BIM-integrated safety risk reviews. Automation in Construction, 94: 290–302
CrossRef Google scholar
[12]
Jo B W, Lee Y S, Khan R M A, Kim J H, Kim D K (2019). Robust Construction Safety System (RCSS) for collision accidents prevention on construction sites. Sensors, 19(4): 932
CrossRef Pubmed Google scholar
[13]
Kanan R, Elhassan O, Bensalem R (2018). An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, 88: 73–86
CrossRef Google scholar
[14]
Kochovski P, Stankovski V (2021). Building applications for smart and safe construction with the DECENTER Fog Computing and Brokerage Platform. Automation in Construction, 124(8): 103562
CrossRef Google scholar
[15]
Li H, Lu M, Chan G, Skitmore M (2015). Proactive training system for safe and efficient precast installation. Automation in Construction, 49: 163–174
CrossRef Google scholar
[16]
Li M, Yu H, Liu P (2018). An automated safety risk recognition mechanism for underground construction at the pre-construction stage based on BIM. Automation in Construction, 91: 284–292
CrossRef Google scholar
[17]
Liu D, Chen J, Hu D, Zhang Z (2019). Dynamic BIM-augmented UAV safety inspection for water diversion project. Computers in Industry, 108: 163–177
CrossRef Google scholar
[18]
Liu X, Li J, Li X (2017). Study of dynamic risk management system for flammable and explosive dangerous chemicals storage area. Journal of Loss Prevention in the Process Industries, 49(Part B): 983–988
CrossRef Google scholar
[19]
Livingston A D, Jackson G, Priestley K (2001). Root causes analysis: Literature review. HSE Contract Research Report
[20]
Malekitabar H, Ardeshir A, Sebt M H, Stouffs R (2016). Construction safety risk drivers: A BIM approach. Safety Science, 82: 445–455
CrossRef Google scholar
[21]
McManus T N (2016). Management of Hazardous Energy: Deactivation, De-Energization, Isolation, and Lockout. CRC Press
[22]
Rathnayaka S, Khan F, Amyotte P (2012). Accident modeling approach for safety assessment in an LNG processing facility. Journal of Loss Prevention in the Process Industries, 25(2): 414–423
CrossRef Google scholar
[23]
Rossi A, Vila Y, Lusiani F, Barsotti L, Sani L, Ceccarelli P, Lanzetta M (2019). Embedded smart sensor device in construction site machinery. Computers in Industry, 108: 12–20
CrossRef Google scholar
[24]
Sakhakarmi S, Park J (2019). Investigation of tactile sensory system configuration for construction hazard perception. Sensors, 19(11): 2527
CrossRef Pubmed Google scholar
[25]
Shahrokhi M, Bernard A (2010). A development in energy flow/barrier analysis. Safety Science, 48(5): 598–606
CrossRef Google scholar
[26]
Skibniewski M J (2014). Information technology applications in construction safety assurance. Journal of Civil Engineering and Management, 20(6): 778–794
CrossRef Google scholar
[27]
Sklet S (2006). Safety barriers: Definition, classification, and performance. Journal of Loss Prevention in the Process Industries, 19(5): 494–506
CrossRef Google scholar
[28]
Sklet S, Hauge S (2004). Reflections on the concept of safety barriers. In: 7th International Conference on Probabilistic Safety Assessment and Management. Berlin: Springer, 94–99
CrossRef Google scholar
[29]
Sobral J, Guedes Soares C (2019). Assessment of the adequacy of safety barriers to hazards. Safety Science, 114: 40–48
CrossRef Google scholar
[30]
Soltanmohammadlou N, Sadeghi S, Hon C K, Mokhtarpour-Khanghah F (2019). Real-time locating systems and safety in construction sites: A literature review. Safety Science, 117: 229–242
CrossRef Google scholar
[31]
Tang S, Shelden D R, Eastman C M, Pishdad-Bozorgi P, Gao X (2019). A review of building information modeling (BIM) and the Internet of Things (IoT) devices integration: Present status and future trends. Automation in Construction, 101: 127–139
CrossRef Google scholar
[32]
Valinejadshoubi M, Moselhi O, Bagchi A, Salem A (2021). Development of an IoT and BIM-based automated alert system for thermal comfort monitoring in buildings. Sustainable Cities and Society, 66(1): 102602
CrossRef Google scholar
[33]
Winge S, Albrechtsen E (2018). Accident types and barrier failures in the construction industry. Safety Science, 105: 158–166
CrossRef Google scholar
[34]
Wu H, Zhao J (2018). An intelligent vision-based approach for helmet identification for work safety. Computers in Industry, 100: 267–277
CrossRef Google scholar
[35]
Xu J, Li Z (2012). Multi-objective dynamic construction site layout planning in fuzzy random environment. Automation in Construction, 27: 155–169
CrossRef Google scholar
[36]
Yan J, Pu W, Liu H, Zhou S, Bao Z (2017). Cooperative target assignment and dwell allocation for multiple target tracking in phased array radar network. Signal Processing, 141: 74–83
CrossRef Google scholar
[37]
Yang K, Ahn C R (2019). Inferring workplace safety hazards from the spatial patterns of workers’ wearable data. Advanced Engineering Informatics, 41: 100924
CrossRef Google scholar
[38]
Zaranejad A, Ahmadi O, Yahyaei E (2016). Designing a quantitative safety checklist for the construction phase of ongoing projects in petrochemical plants. Journal of Occupational Health and Epidemiology, 5(1): 1–9
CrossRef Google scholar
[39]
Zhang M, Cao T, Zhao X (2017). Applying sensor-based technology to improve construction safety management. Sensors, 17(8): 1841
CrossRef Google scholar
[40]
Zhang S, Boukamp F, Teizer J (2015). Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis. Automation in Construction, 52: 29–41
CrossRef Google scholar
[41]
Zhong R Y, Peng Y, Xue F, Fang J, Zou W, Luo H, Ng S T, Lu W, Shen G Q P, Huang G Q (2017). Prefabricated construction enabled by the Internet-of-Things. Automation in Construction, 76: 59–70
CrossRef Google scholar
[42]
Zhou C, Ding L Y (2017). Safety barrier warning system for underground construction sites using Internet-of-Things technologies. Automation in Construction, 83: 372–389
CrossRef Google scholar

Acknowledgments

The authors thank Sinopec-SK (Wuhan) Petrochemical Company Limited.

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(6371 KB)

Accesses

Citations

Detail

Sections
Recommended

/