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Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction Projects

Author

Listed:
  • Chih-Hsiung Chang

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Mei-Ling Chuang

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
    Taoyuan Metro Corporation, Taoyuan 33743, Taiwan)

  • Jia-Cheng Tan

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Chuen-Chyi Hsieh

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

  • Chien-Cheng Chou

    (Information Technology for Disaster Prevention (IT) Program, Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan)

Abstract

With the trend of sustainable development growing worldwide, both the numbers of new mega building construction projects and renovations to existing high-rise buildings are increasing. At such construction sites, most construction workers can be described as performing various activities in indoor spaces. The literature shows that the indoor safety protection measures in such construction sites are often imperfect, resulting in an endless stream of incidents such as falls. Thus, this research aims at developing a flexible indoor safety warning system, based on Wi-Fi-generated channel state information (CSI), for monitoring the construction workers approaching restricted areas or floor openings. In the proposed approach, construction workers do not have to carry any sensors, and each indoor space only needs to have the specified Wi-Fi devices installed. Since deep learning methods are employed to analyze the CSI data collected, the total deployment time, including setting up the Wi-Fi devices and performing data collection and training work, has been measured. Efficiency and effectiveness of the developed system, along with further developments, have been evaluated and discussed by 12 construction safety experts. It is expected that the proposed approach can be enhanced to accommodate other types of safety hazards and be implemented in all mega building construction projects so that the construction workers can have safer working environments.

Suggested Citation

  • Chih-Hsiung Chang & Mei-Ling Chuang & Jia-Cheng Tan & Chuen-Chyi Hsieh & Chien-Cheng Chou, 2022. "Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction Projects," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15034-:d:971957
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    References listed on IDEAS

    as
    1. Qingwei Xu & Kaili Xu, 2021. "Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    2. Mina Jowkar & Alenka Temeljotov-Salaj & Carmel Margaret Lindkvist & Marit Støre-Valen, 2022. "Sustainable building renovation in residential buildings: barriers and potential motivations in Norwegian culture," Construction Management and Economics, Taylor & Francis Journals, vol. 40(3), pages 161-172, March.
    3. J�rgen Melzner & Sijie Zhang & Jochen Teizer & Hans-Joachim Bargst�dt, 2013. "A case study on automated safety compliance checking to assist fall protection design and planning in building information models," Construction Management and Economics, Taylor & Francis Journals, vol. 31(6), pages 661-674, June.
    4. Zhenyu Zhang & Ken-Yu Lin & Jia-Hua Lin, 2021. "Factors Affecting Material-Cart Handling in the Roofing Industry: Evidence for Administrative Controls," IJERPH, MDPI, vol. 18(4), pages 1-19, February.
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    Cited by:

    1. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.

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