A review of data-driven accident prevention systems

Integrating real-time safety management in the civil infrastructure context

A. Assadzadeh, M. Arashpour, A. Rashidi, A. Bab-Hadiashar, S. Fayezi

Research output: Contribution to conferencePaperOther

Abstract

Statistical reports point to the fact that civil infrastructure projects remain hazardous working environments. Despite the implementation of various safety procedures, the frequency and cost of work-related injuries are significant. Improvements in sensor technologies, wireless communication and processing power of computers as well as advancements in machine learning and computer vision are now enabling data-driven systems as effective safety barriers for accident prevention. In recent years, many researchers have studied various methods of leveraging technology to improve safety in civil infrastructure projects. However, previous investigations have not produced a thorough analysis of the practicality of those approaches. While considerable progress has been made in developing methods to improve construction safety, few studies have focused on implementation of data-driven real-time accident prevention systems to effectively minimize risk in the event where other safety measures have failed or been absent. Motivated to facilitate the development of such method, this paper carries out thorough analysis of the field and its trends, identifies research gaps, provides a discussion of recent advancements, and highlights future research directions to help researchers gain an up-to-date overview of the state-of-the-art and navigate through this domain efficiently.

Original languageEnglish
Pages289-296
Number of pages8
Publication statusPublished - 1 Jan 2019
EventInternational Symposium on Automation and Robotics in Construction 2019 - Banff, Canada
Duration: 21 May 201924 May 2019
Conference number: 36th
https://isarc2019.org/

Conference

ConferenceInternational Symposium on Automation and Robotics in Construction 2019
Abbreviated titleISARC 2019
CountryCanada
CityBanff
Period21/05/1924/05/19
Internet address

Keywords

  • Automation
  • Behavior related accidents
  • Building information modeling (BIM)
  • Construction safety management
  • Information Technology
  • Infrastructure sector
  • Machine learning
  • Neural networks
  • Object detection
  • Risk analysis and control
  • Sensors

Cite this

Assadzadeh, A., Arashpour, M., Rashidi, A., Bab-Hadiashar, A., & Fayezi, S. (2019). A review of data-driven accident prevention systems: Integrating real-time safety management in the civil infrastructure context. 289-296. Paper presented at International Symposium on Automation and Robotics in Construction 2019, Banff, Canada.
Assadzadeh, A. ; Arashpour, M. ; Rashidi, A. ; Bab-Hadiashar, A. ; Fayezi, S. / A review of data-driven accident prevention systems : Integrating real-time safety management in the civil infrastructure context. Paper presented at International Symposium on Automation and Robotics in Construction 2019, Banff, Canada.8 p.
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Assadzadeh, A, Arashpour, M, Rashidi, A, Bab-Hadiashar, A & Fayezi, S 2019, 'A review of data-driven accident prevention systems: Integrating real-time safety management in the civil infrastructure context' Paper presented at International Symposium on Automation and Robotics in Construction 2019, Banff, Canada, 21/05/19 - 24/05/19, pp. 289-296.

A review of data-driven accident prevention systems : Integrating real-time safety management in the civil infrastructure context. / Assadzadeh, A.; Arashpour, M.; Rashidi, A.; Bab-Hadiashar, A.; Fayezi, S.

2019. 289-296 Paper presented at International Symposium on Automation and Robotics in Construction 2019, Banff, Canada.

Research output: Contribution to conferencePaperOther

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Assadzadeh A, Arashpour M, Rashidi A, Bab-Hadiashar A, Fayezi S. A review of data-driven accident prevention systems: Integrating real-time safety management in the civil infrastructure context. 2019. Paper presented at International Symposium on Automation and Robotics in Construction 2019, Banff, Canada.