Journal of Engineering, Project, and Production Management, 2024, 14(4), 0036
A Knowledge-Driven Approach to Automate Job Hazard Analysis Process
1Ph.D. Student, UniSA STEM,
University of South Australia, Adelaide, SA 5000, Australia, E-mail:
thalpe_panditha_wattage_sonali.pandithawatta@mymail.unisa.edu.au
(corresponding author).
2Professor, UniSA STEM, University of South Australia,
Adelaide, SA 5000, Australia, E-mail: rameez.rameezdeen@unisa.edu.au
3Assistant Professor, Department of Civil and Environmental
Engineering, Hongik University, Seoul 04066, Republic of Korea, E-mail:
jun.ahn@hongik.ac.kr
4Professor, UniSA STEM, University of South Australia,
Adelaide, SA 5000, Australia, E-mail: christopher.chow@unisa.edu.au
5Associate Professor, UniSA STEM, University of South
Australia, Adelaide, SA 5000, Australia, E-mail: nima.gorjianJolfaei@unisa.edu.au
Project Management
Received November 24, 2023; received revision November 21, 2024; accepted November 21, 2024
Available online November 28, 2024
Abstract:
Automating the job
hazard analysis (JHA) process is an urgent requirement in the
construction safety management field due to limitations of the
conventional process. The manual nature of conducting the JHA and the
dynamic environment of construction sites make it necessary to perform
the analysis before commencing the job and to then regularly update it
in accordance with changes in the construction plans. With this in mind,
this research aims to develop an automated approach to support safety
personnel during the JHA process.
In seeking to automate the JHA process, the nature of construction
accidents, hazards and risk assessment needs to be studied in light of
the theoretical knowledge on accident causation. Thus, this research was
designed according to the constructive research approach to develop a
job hazard analysis knowledge graph (JHAKG) to automate the JHA process.
The JHAKG incorporated an ontology (O-JHAKG) built according to the
systematic ontology development method, METHONTOLOGY, which formalises
both explicit and implicit knowledge inherent in the JHA process. The
data were imported to the JHAKG from an incident database using
rule-based natural language processing (NLP) which helped to extract
implicit information not evident in the traditional JHA document. The
validation of the JHAKG was conducted in two stages: the first stage
validated the information extraction process by calculating performance
metrics, while the second stage validated the data population process
and the JHAKG's reasoning capability. The overall research resulted in a
comprehensive JHAKG with advanced inferencing capabilities which can
assist safety personnel in effectively executing the JHA process.
Keywords: Construction industry, knowledge graph, nlp, ontology, safety management.
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Citation: Pandithawatta, S., Rameezdeen, R., Ahn, S., Chow, C. W. K., and Gorjian, N. (2025). A Knowledge-Driven Approach to Automate Job Hazard Analysis Process. Journal of Engineering, Project, and Production Management, 14(4), 0036.
DOI:
10.32738/JEPPM-2024-0036
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