Ontological Modeling of Tacit Knowledge for Automating Job Hazard Analysis in Construction
Publication: Journal of Management in Engineering
Volume 40, Issue 5
Abstract
Due to the dynamic nature of work environments and conditions in construction, it is necessary to perform a job hazard analysis (JHA) prior to the commencement of hazardous jobs, and regularly review and update it. JHA is considered an intellectual activity subject to substantial influence by the experience and knowledge of the individuals conducting the analysis. Given the manual nature of JHA in current practice, its thorough preparation and use are time-consuming and laborious; thus, there is a great need to automate it. Against this background, this research aimed to develop a conceptual ontological model that can support the automation of JHA processes, including the tacit knowledge possessed by experts to facilitate automation. A JHA document analysis and a qualitative Delphi study were adopted to identify the concepts and associations embedded in JHA. An abductive data analysis approach was used with the guidance of a theoretical understanding of the systems model of construction accident causation to analyze the data collected from JHA documents and interviews. The findings offer valuable insights into important entities, subentities, and relationships that are associated with hazard identification and risk assessment, which form the basis for developing a conceptual ontological model. Such an ontology can facilitate the automation of JHA with an enhanced level of reasoning capability, through which the efficiency and effectiveness of JHA on construction sites can be improved.
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Data Availability Statement
The data used in this research primarily consist of interview material collected from safety experts involved in JHA. Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was partially supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1F1A1074448) and the 2022 Hongik University Research Fund.
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Journal of Management in Engineering
Volume 40 • Issue 5 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: May 21, 2023
Accepted: Apr 17, 2024
Published online: Jul 12, 2024
Published in print: Sep 1, 2024
Discussion open until: Dec 12, 2024
ASCE Technical Topics:
- Analysis (by type)
- Automation and robotics
- Business management
- Construction engineering
- Construction management
- Data analysis
- Disaster risk management
- Dynamic analysis
- Engineering fundamentals
- Methodology (by type)
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Risk management
- Safety
- Systems engineering
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