Applying Association Rule Mining to Explore Unsafe Behaviors in the Indonesian Construction Industry

Rossy Armyn Machfudiyanto, Jieh Haur Chen, Yusuf Latief, Titi Sari Nurul Rachmawati, Achmad Muhyidin Arifai, Naufal Firmansyah

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The frequency of work accidents in construction projects is relatively high. One contributing factor to work accidents is unsafe behavior by workers at construction sites. In Indonesia, this is the first study to investigate 2503 instances of unsafe behavior that occurred across Indonesian construction projects in relation to their attributes to obtain insightful knowledge by using the association rule mining (ARM) method. Association rule mining was used to explore the database. As a result, two consolidated rules were obtained. The most frequent unsafe behaviors were workers putting tools and materials in random places, workers not attaching safety lines at provided places, and workers moving work tools and materials in ways that were not in accordance with procedures. These unsafe behaviors were associated with accident types of falling, and being struck or cut by items, as well as violations of Manpower and Transmigration Ministerial Regulation 01/1980, and Manpower Ministerial Regulation 09/2016. The ARM results were evaluated with a reliability evaluation method before being validated by construction safety experts. Hence, the findings are reliable to be used as guideline information for safety trainers to prioritize related safety trainings and for safety inspectors when carrying out inspections on construction sites. As a result, safety management and safety performance can increase significantly.

Original languageEnglish
Article number5261
JournalSustainability (Switzerland)
Volume15
Issue number6
DOIs
Publication statusPublished - Mar 2023

Keywords

  • association rules
  • construction safety
  • data mining
  • unsafe behavior

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