Pedro, Akeem and Pham-Hang, Anh-Tuan and Nguyen, Phong Thanh and Pham, Hai Chien (2022): Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies. Published in: International Journal of Environmental Research & Public Health , Vol. 19, No. 2 (11 January 2022): 01-18.
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Abstract
Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new “open” information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.
Item Type: | MPRA Paper |
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Original Title: | Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies |
English Title: | Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies |
Language: | English |
Keywords: | construction safety, information sharing, knowledge graph, linked data, ontology, semantic web, data-driven, knowledge engineering, knowledge management, accident prevention |
Subjects: | D - Microeconomics > D8 - Information, Knowledge, and Uncertainty D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief ; Unawareness L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L74 - Construction L - Industrial Organization > L8 - Industry Studies: Services > L82 - Entertainment ; Media N - Economic History > N6 - Manufacturing and Construction |
Item ID: | 112007 |
Depositing User: | Dr. Phong Thanh Nguyen |
Date Deposited: | 16 Feb 2022 16:18 |
Last Modified: | 16 Feb 2022 16:18 |
References: | 1. Mckinsey Global Institute. The Next Normal in Construction: How Disruption Is Reshaping the World’s Largest Ecosystem. Available online: https://www.mckinsey.com/business-functions/operations/our-insights/the-next-normal-in-constructionhow-disruption-is-reshaping-the-worlds-largest-ecosystem (accessed on 14 October 2021). 2. Pedro, A.; Pham, H.C.; Kim, J.U.; Park, C. Development and evaluation of context-based assessment system for visualizationenhanced construction safety education. Int. J. Occup. Saf. Ergon. 2020, 26, 811–823. 3. Pedro, A.; Le, Q.T.; Park, C.S. Framework for integrating safety into construction methods education through interactive virtual reality. J. Prof. Issues Eng. Educ. Pract. 2016, 142, 04015011. 4. Pham, H.C.; Dao, N.N.; Cho, S.; Nguyen, P.T.; Pham-Hang, A.T. Construction hazard investigation leveraging object anatomization on an augmented photoreality platform. Appl. Sci. 2019, 9, 4477. 5. Uddin, S.M.; Albert, A.; Alsharef, A.; Pandit, B.; Patil, Y.; Nnaji, C. Hazard Recognition Patterns Demonstrated by Construction Workers. Int. J. Environ. Res. Public Health 2020, 17, 7788. 6. Hussain, R.; Pedro, A.; Lee, D.Y.; Pham, H.C.; Park, C.S. Impact of safety training and interventions on training-transfer: Targeting migrant construction workers. Int. J. Occup. Saf. Ergon. 2018, 26, 272–284. 7. Pham, K.T.; Vu, D.N.; Hong, P.L.H.; Park, C. 4D-BIM-Based Workspace Planning for Temporary Safety Facilities in Construction SMEs. Int. J. Environ. Res. Public Health 2020, 17, 3403. 8. Pham, H.C.; Pedro, A.; Le, Q.T.; Lee, D.Y.; Park, C.S. Interactive safety education using building anatomy modelling. Univers. Access Inf. Soc. 2019, 18, 269–285. 9. Carpio-de Los Pinos, A.J.; González-García, M.D. Development of the protocol of the occupational risk assessment method for construction works: Level of Preventive Action. Int. J. Environ. Res. Public Health 2020, 17, 6369. 10. Sousa, V.; Almeida, N.M.; Dias, L.A. Risk-based management of occupational safety and health in the construction industry–Part2: Quantitative model. Saf. Sci. 2015, 74, 184–194. 11. Pi, Z.; Gao, X.; Chen, L.; Liu, J. The New Path to Improve Construction Safety Performance in China: An Evolutionary Game Theoretic Approach. Int. J. Environ. Res. Public Health 2019, 16, 2443. 12. Pedro, A.; Chien, P.H.; Park, C.S. Towards a competency-based vision for construction safety education. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2018; Volume 143, p. 012051. 13. Meng, X.; Chan, A.H. Current states and future trends in safety research of construction personnel: A quantitative analysis based on social network approach. Int. J. Environ. Res. Public Health 2021, 18, 883. 14. Benner, L., Jr. Accident data for the Semantic Web. Saf. Sci. 2012, 50, 1431–1437. 15. Pedro, A.; Lee, D.Y.; Hussain, R.; Park, C.S. Linked data system for sharing construction safety information. In International Symposium on Automation and Robotics in Construction (ISARC); IAARC Publications: Taipei, Taiwan, 2017; Volume 34. 16. Lee, D.Y.; Chi, H.L.; Wang, J.; Wang, X.; Park, C.S. A linked data system framework for sharing construction defect information using ontologies and BIM environments. Autom. Constr. 2016, 68, 102–113. 17. Shadbolt, N.; O’Hara, K. Linked Data in Government. IEEE Internet Comput. 2013, 17, 72–77. 18. Rajabi, E. Towards linked open government data in Canada. Int. J. Metadata Semant. Ontol. 2020, 14, 209–217. 19. Janev, V.; Mijovi´c, V.; Vraneš, S. Using the linked data approach in European e-government systems: Example from Serbia. Int. J.Semant. Web Inf. Syst. 2018, 14, 27–46. 20. Kostkova, P.; Brewer, H.; de Lusignan, S.; Fottrell, E.; Goldacre, B.; Hart, G.; Tooke, J. Who owns the data? Open data for healthcare. Front. Public Health 2016, 4,7. 21. Bariseviˇcius, G.; Coste, M.; Geleta, D.; Juric, D.; Khodadadi, M.; Stoilos, G.; Zaihrayeu, I. Supporting digital healthcare services using semantic web technologies. In International Semantic Web Conference; Springer: Berlin/Heidelberg, Germany, 2018; pp. 291–306. 22. Li, R.Y.M.; Chau, K.W.; Lu, W.; Ho, D.C.W.; Shoaib, M.; Meng, L. Construction hazard awareness and construction safety knowledge sharing epistemology. In International Conference on Smart Infrastructure and Construction; ICE Publishing United Kingdom: London, UK, 2019; pp. 283–290. 23. Sydnes, A.K.; Sydnes, M.; Hamnevoll, H. Learning from crisis: The 2015 and 2017 avalanches in Longyearbyen. Saf. Sci. 2021, 134, 105045. 24. Carroll, J.S.; Fahlbruch, B. “The gift of failure: New approaches to analyzing and learning from events and near-misses.” Honoring the contributions of Bernhard Wilpert. Saf. Sci. 2011, 49, 1–4. 25. Baker, H.; Smith, S.; Masterton, G.; Hewlett, B. Data-led learning: Using natural language processing (nlp) and machine learning to learn from construction site safety failures. Management 2020, 356, 365. 26. Wasilkiewicz, K. Information flow and knowledge transfer of accident investigation results in the Norwegian construction industry. In Safety and Reliability–Safe Societies in a Changing World; Proceedings of ESREL: Trondheim, Norway, 2018. 27. Kim, T.; Chi, S. Accident case retrieval and analyses: Using natural language processing in the construction industry. J. Constr. Eng. Manag. 2019, 145, 04019004. 28. Gibb, A.; Lingard, H.; Behm, M.; Cooke, T. Construction accident causality: Learning from different countries and differing consequences. Constr. Manag. Econ. 2014, 32, 446–459. 29. Dass, A.; Aksoy, C.; Dimitriou, A.; Theodoratos, D. Exploiting semantic result clustering to support keyword search on linked data. In International Conference on Web Information Systems Engineering; Springer: Berlin Heidelberg, Germany, 2014; pp. 448–463. 30. Su, Y.; Yang, S.; Liu, K.; Hua, K.; Yao, Q. Developing a case-based reasoning model for safety accident pre-control and decision making in the construction industry. Int. J. Environ. Res. Public Health 2019, 16, 1511. 31. Le, Q.T.; Lee, D.Y.; Park, C.S. A social network system for sharing construction safety and health knowledge. Autom. Constr. 2014, 46, 30–37. 32. Michalowski, M.; Wilk, S.; Michalowski, W.; O’sullivan, D.; Bonaccio, S.; Parimbelli, E.; Carrier, M.; Le Gal, G.; Kingwell, S.; Peleg, M. A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment. 33. Wu, H.; Zhong, B.; Medjdoub, B.; Xing, X.; Jiao, L. An Ontological Metro Accident Case Retrieval Using CBR and NLP. Appl. Sci. 2020, 10, 5298. 34. Guo, B.H.; Goh, Y.M. Ontology for design of active fall protection systems. Autom. Constr. 2017, 82, 138–153. 35. Zhang, S.; Boukamp, F.; Teizer, J. Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Autom. Constr. 2015, 52, 9–41. 36. Lu, Y.; Li, Q.; Zhou, Z.; Deng, Y. Ontology-based knowledge modeling for automated construction safety checking. Saf. Sci. 2015, 79, 11–18. 37. Bizer, C.; Heath, T.; Berners-Lee, T. Linked data: Principles and state of the art. World Wide Web Conf. 2008, 1, 40. 38. Nicholson, D.N.; Greene, C.S. Constructing knowledge graphs and their biomedical applications. Comput. Struct. Biotechnol. J. 2020, 18, 1414–1428. 39. Hype Cycle. Available online: https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-forai-2021 (accessed on 14 October 2021). 40. Pauwels, P.; McGlinn, K.; Torma, S.; Beetz, J. Linked data. In Building Information Modeling; Springer: Berlin/Heidelberg, Germany, 2018; pp. 181–197. 41. He, D.; Li, Z.; Wu, C.; Ning, X. An e-commerce platform for industrialized construction procurement based on BIM and linked data. Sustainability 2018, 10, 2613. 42. Farghaly, K.; Abanda, F.H.; Vidalakis, C.; Wood, G. BIM-linked data integration for asset management. Built Environ. Proj. Asset Manag. 2019, 9, 489–502. 43. Hu, S.; Wang, J.; Hoare, C.; Li, Y.; Pauwels, P.; O’Donnell, J. Building energy performance assessment using linked data and cross-domain semantic reasoning. Autom. Constr. 2021, 124, 103580. 44. Jiang, Y.; Gao, X.; Su, W.; Li, J. Systematic knowledge management of construction safety standards based on knowledge graphs: A case study in China. Int. J. Environ. Res. Public Health 2021, 18, 10692. 45. Soman, R.K.; Molina-Solana, M.; Whyte, J.K. Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction. Autom. Constr. 2020, 120, 103369. 46. Pauwels, P.; Zhang, S.; Lee, Y.C. Semantic web technologies in AEC industry: A literature overview. Autom. Constr. 2017, 73, 145–165. 47. Batres, R.; Fujihara, S.; Shimada, Y.; Fuchino, T. The use of ontologies for enhancing the use of accident information. Process Saf. Environ. Prot. 2014, 92, 119–130. 48. KGLAB. Available online: https://derwen.ai/docs/kgl/ (accessed on 20 November 2021). 49. Django. Available online: https://www.djangoproject.com (accessed on 20 November 2021). 50. Apache Jena Fuseki. Available online: https://jena.apache.org/fuseki2 (accessed on 20 November 2021). 51. Construction Safety Ontology. Available online: https://github.com/lanrepedro3/constructionsafetyontology (accessed on 25 October 2021). 52. HermiT OWL Reasoner. Available online: http://www.hermit-reasoner.com (accessed on 20 November 2021). |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/112007 |