Quantifying the Effectiveness of IoT Technologies for Accident Prevention

Chang Jae Yeo, Jung Ho Yu, Youngcheol Kang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The Internet of Things (IoT) has attracted attention in recent years as a way to prevent construction site accidents. Although various IoT technologies have been tested for the purpose of safety management, few have been implemented in actual projects. One possible reason is that the effectiveness of these technologies has rarely been calculated. In this study, a method for quantitatively evaluating the effectiveness of IoT technologies for accident prevention is presented. Taking the domino theory of accident causation into account, this method has three aspects: the degree of the causes of accidents that an IoT technology prevents, association between accident types and their causes, and frequency of each accident type. To quantify these, two different types of survey were conducted, and statistical records about construction accidents by type were used. To test the applicability of this method, the effectiveness of two IoT technologies was calculated. The method successfully quantified how much each technology contributes to preventing certain types of accident as well as the overall accident-prevention effect. The proposed method can enable practitioners to assess the effectiveness of certain IoT technologies, which will be useful in justifying investments in the technology. The method will lead to deploying more IoT technologies for safety management, which will eventually contribute to decreasing accidents in the construction industry.

Original languageEnglish
Article number04020054
JournalJournal of Management in Engineering
Volume36
Issue number5
DOIs
Publication statusPublished - 2020 Sep 1

Bibliographical note

Publisher Copyright:
© 2020 American Society of Civil Engineers.

All Science Journal Classification (ASJC) codes

  • Industrial relations
  • Engineering(all)
  • Strategy and Management
  • Management Science and Operations Research

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