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 and Naufal Firmansyah
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Rossy Armyn Machfudiyanto: Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Jieh-Haur Chen: Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
Yusuf Latief: Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Titi Sari Nurul Rachmawati: Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Achmad Muhyidin Arifai: Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
Naufal Firmansyah: Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia

Sustainability, 2023, vol. 15, issue 6, 1-16

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.

Keywords: construction safety; association rules; unsafe behavior; data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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