TY - GEN
T1 - Implementing CRISP-DM as an Association Rule Mining Model Between Construction Activities and Potential Safety Hazards
T2 - 6th International Conference on Rehabilitation and Maintenance in Civil Engineering, ICRMCE 2024
AU - Machfudiyanto, Rossy Armyn
AU - Primaputra, Khrisna
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The construction industry is a high-risk sector and is considered the most hazardous industry worldwide. The impact of construction accidents can be devastating to a country’s macroeconomic condition. This emphasizes the need for effective safety control systems and preventive measures, particularly in identifying safety hazards. The use of association rule mining for construction safety has been increasingly explored in several research. However, there is currently no standard model to assist practitioners in utilizing data mining to identify hazards in construction projects. CRISP-DM, as the de facto standard data mining model, can be implemented to serve as a standard and guide for practitioners. The primary objective of this research is to identify a suitable conceptual framework for construction safety hazard exploration using CRISP-DM. The methods used to develop the framework were literature review and expert judgment. The results of this study indicate that the CRISP-DM model can be applied in the construction industry, but with the support of an improved reporting system and accident data documentation.
AB - The construction industry is a high-risk sector and is considered the most hazardous industry worldwide. The impact of construction accidents can be devastating to a country’s macroeconomic condition. This emphasizes the need for effective safety control systems and preventive measures, particularly in identifying safety hazards. The use of association rule mining for construction safety has been increasingly explored in several research. However, there is currently no standard model to assist practitioners in utilizing data mining to identify hazards in construction projects. CRISP-DM, as the de facto standard data mining model, can be implemented to serve as a standard and guide for practitioners. The primary objective of this research is to identify a suitable conceptual framework for construction safety hazard exploration using CRISP-DM. The methods used to develop the framework were literature review and expert judgment. The results of this study indicate that the CRISP-DM model can be applied in the construction industry, but with the support of an improved reporting system and accident data documentation.
KW - Association rule mining
KW - Construction safety
KW - CRISP-DM
KW - Hazard identification
UR - https://www.scopus.com/pages/publications/105011099500
U2 - 10.1007/978-981-96-4694-4_42
DO - 10.1007/978-981-96-4694-4_42
M3 - Conference contribution
AN - SCOPUS:105011099500
SN - 9789819646937
T3 - Lecture Notes in Civil Engineering
SP - 415
EP - 422
BT - Proceedings of the 6th International Conference on Rehabilitation and Maintenance in Civil Engineering - Volume 2 - ICRMCE 2024
A2 - Kristiawan, Stefanus A.
A2 - Tsai, Keh-Chyuan
A2 - Shahin, Mohamed
A2 - Sam, Abdul Rahman Mohd
A2 - Hai, Pham Dinh
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 July 2024 through 5 July 2024
ER -