A Knowledge-Driven Approach to Automate Job Hazard Analysis Process
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 be...
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| Format: | Article |
| Language: | English |
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Engineering, Project, and Production Management (EPPM)
2024-12-01
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| Series: | Journal of Engineering, Project, and Production Management |
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| Online Access: | http://www.ppml.url.tw/EPPM_Journal/volumns/14_04_December_2024/20230190.pdf |
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| author | Sonali Pandithawatta Raufdeen Rameezdeen Seungjun Ahn Christopher W. K. Chow Nima Gorjian |
| author_facet | Sonali Pandithawatta Raufdeen Rameezdeen Seungjun Ahn Christopher W. K. Chow Nima Gorjian |
| author_sort | Sonali Pandithawatta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4fa60c5480c54ab28356bec841300cd5 |
| institution | OA Journals |
| issn | 2221-6529 2223-8379 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Engineering, Project, and Production Management (EPPM) |
| record_format | Article |
| series | Journal of Engineering, Project, and Production Management |
| spelling | doaj-art-4fa60c5480c54ab28356bec841300cd52025-08-20T02:06:44ZengEngineering, Project, and Production Management (EPPM)Journal of Engineering, Project, and Production Management2221-65292223-83792024-12-0114411210.32738/JEPPM-2024-0036A Knowledge-Driven Approach to Automate Job Hazard Analysis ProcessSonali Pandithawatta0Raufdeen Rameezdeen1Seungjun Ahn2Christopher W. K. Chow3Nima Gorjian4University of South AustraliaUniversity of South AustraliaHongik UniversityUniversity of South AustraliaUniversity of South AustraliaAutomating 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.http://www.ppml.url.tw/EPPM_Journal/volumns/14_04_December_2024/20230190.pdfconstruction industryknowledge graphnlpontologysafety management |
| spellingShingle | Sonali Pandithawatta Raufdeen Rameezdeen Seungjun Ahn Christopher W. K. Chow Nima Gorjian A Knowledge-Driven Approach to Automate Job Hazard Analysis Process Journal of Engineering, Project, and Production Management construction industry knowledge graph nlp ontology safety management |
| title | A Knowledge-Driven Approach to Automate Job Hazard Analysis Process |
| title_full | A Knowledge-Driven Approach to Automate Job Hazard Analysis Process |
| title_fullStr | A Knowledge-Driven Approach to Automate Job Hazard Analysis Process |
| title_full_unstemmed | A Knowledge-Driven Approach to Automate Job Hazard Analysis Process |
| title_short | A Knowledge-Driven Approach to Automate Job Hazard Analysis Process |
| title_sort | knowledge driven approach to automate job hazard analysis process |
| topic | construction industry knowledge graph nlp ontology safety management |
| url | http://www.ppml.url.tw/EPPM_Journal/volumns/14_04_December_2024/20230190.pdf |
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