Skip to main content
SpringerLink
Log in

Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole. The existing research on construction accidents heavily relies on expert evaluations, which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity. However, expert opinions provided in construction accident reports offer a valuable source of knowledge that can be extracted and utilized to enhance safety management. Today this valuable resource can be mined as the advent of artificial intelligence has opened up significant opportunities to advance construction site safety. Ontology represents an attractive representation scheme. Though ontology has been used in construction safety to solve the problem of information heterogeneity using formal conceptual specifications, the establishment and development of ontologies that utilize construction accident reports are currently in an early stage of development and require further improvements. Moreover, research on the exploration of incorporating deep learning methodologies into construction safety ontologies for predicting construction safety incidents is relatively limited. This paper describes a novel approach to improving the performance of accident prediction models by incorporating ontology into a deep learning model. First, a domain word discovery algorithm, based on mutual information and adjacency entropy, is used to analyze the causes of accidents mentioned in construction reports. This analysis is then combined with technical specifications and the literature in the field of construction safety to build an ontology encompassing unsafe factors related to construction accidents. By employing a Translating on Hyperplane (TransH) model, the reports are transformed into conceptual vectors using the constructed ontology. Building on this foundation, we propose a Text Convolutional Neural Network (TextCNN) model that incorporates the ontology specifically designed for construction accidents. We compared the performance of the TextCNN model against five traditional machine learning models, namely Naive Bayes, support vector machine, logistic regression, random forest, and multilayer perceptron, using three different data sets: One-Hot encoding, word vector, and conceptual vectors. The results indicate that the TextCNN model integrated with the ontology outperformed the other models in terms of performance achieving an impressive accuracy rate of 88% and AUC value of 0.92.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Aneziris ON, Topali E, Papazoglou IA (2012) Occupational risk of building construction. Reliab Eng Syst Saf 105:36–46

    Article  Google Scholar 

  2. Aziz A, Ahmed S, Khan FI (2019) An ontology-based methodology for hazard identification and causation analysis. Process Saf Environ Prot 123:87–98

    Article  Google Scholar 

  3. Basaran I, Yilmaz S (2016) Developing rail safety competencies based on accident and incident investigations: using root cause taxonomies to learn from accidents. In: 2016 IEEE International conference on industrial engineering and engineering management (IEEM). IEEE, pp 481–485

  4. Bashar MA, Li Y (2018) Interpretation of text patterns. Data Min Knowl Discov 32:849–884

    Article  MathSciNet  Google Scholar 

  5. Benjaoran V, Bhokha S (2010) An integrated safety management with construction management using 4d cad model. Saf Sci 48:395–403

    Article  Google Scholar 

  6. Cao T, Mu W, Gou J, Peng L (2020) A study of risk relevance reasoning based on a context ontology of railway accidents. Risk Anal 40:1589–1611

    Article  Google Scholar 

  7. Carbonari A, Giretti A, Naticchia B (2011) A proactive system for real-time safety management in construction sites. Autom Constr 20:686–698

    Article  Google Scholar 

  8. Chen WT, Bria TA (2022) A review of ontology-based safety management in construction. Sustainability 15:413

    Article  Google Scholar 

  9. Chen Y (2015) Convolutional neural network for sentence classification. Master’s thesis. University of Waterloo

  10. Cheng EW, Ryan N, Kelly S (2012) Exploring the perceived influence of safety management practices on project performance in the construction industry. Saf Sci 50:363–369

    Article  Google Scholar 

  11. Chi NW, Lin KY, Hsieh SH (2014) Using ontology-based text classification to assist job hazard analysis. Adv Eng Inform 28:381–394

  12. Eardley W, Ashe D, Fletcher B (2016) An ontology engineering approach to user profiling for virtual tours of museums and galleries. Int J Knowl Eng 2:85–91

    Article  Google Scholar 

  13. Farghaly K, Soman RK, Collinge W, Mosleh MH, Manu P, Cheung CM (2022) Construction safety ontology development and alignment with industry foundation classes (IFC). J Inf Technol Constr 27:94–108

    Google Scholar 

  14. Fernández-López M, Gómez-Pérez A, Juristo N (1997) Methontology: from ontological art towards ontological engineering. In: Proceedings of the ontological engineering AAAI-97 Spring symposium series

  15. Fu Y, Wen P, Wu J, Shu Y Knowledge graph-based policy analysis from a hybrid prospect of external attributes and internal characteristics under carbon peaking and carbon neutrality goal. Available at SSRN 4384948

  16. Gao S, Ren G, Li H (2022) Knowledge management in construction health and safety based on ontology modeling. Appl Sci 12:8574

    Article  Google Scholar 

  17. Ge J, Zhang Y, Chen S, Xu K, Yao X, Li J, Liu B, Yan F, Wu C, Li S (2022) Accident causation models developed in china between 1978 and 2018: review and comparison. Saf Sci 148:105653

    Article  Google Scholar 

  18. Gruninger M (1995) Methodology for the design and evaluation of ontologies. In: Proceedings of IJCAI’95, workshop on basic ontological issues in knowledge sharing, pp 85–91

  19. Hai N, Gong D, Liu S (2021) Ontology knowledge base combined with Bayesian networks for integrated corridor risk warning. Comput Commun 174:190–204

    Article  Google Scholar 

  20. Halder A, Batra S (2023) Application of predictive analytics in built environment research: a comprehensive bibliometric study to explore knowledge domains and future research agenda. Arch Comput Methods Eng 1–26

  21. Huang JH, Powers D (2003) Chinese word segmentation based on contextual entropy. In: Proceedings of the 17th Pacific Asia conference on language, information and computation, pp 152–158

  22. Jiang X, Tan AH (2009) Learning and inferencing in user ontology for personalized semantic web search. Inf Sci 179:2794–2808

    Article  Google Scholar 

  23. Jiang X, Wang S, Wang J, Lyu S, Skitmore M (2020) A decision method for construction safety risk management based on ontology and improved CBR: example of a subway project. Int J Environ Res Public Health 17:3928

    Article  Google Scholar 

  24. Jin R, Zou Y, Gidado K, Ashton P, Painting N (2019) Scientometric analysis of bim-based research in construction engineering and management. Eng Constr Archit Manag 26:1750–1776

    Article  Google Scholar 

  25. Johansen KW, Schultz C, Teizer J (2023) Hazard ontology and 4d benchmark model for facilitation of automated construction safety requirement analysis. Comput Aided Civ Infrastruct Eng

  26. Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759

  27. Kamel MN, Lee AY, Powers EC (2007) A methodology for developing ontologies using the ontology web language (owl) In: ICEIS (4), pp 261–268

  28. Kastrati Z, Imran AS, Yayilgan SY (2019) The impact of deep learning on document classification using semantically rich representations. Inf Process Manag 56:1618–1632

    Article  Google Scholar 

  29. Kim HM, Fox MS, Gruninger M (1995) An ontology of quality for enterprise modelling. In: Proceedings 4th IEEE workshop on enabling technologies: infrastructure for collaborative enterprises (WET ICE’95), IEEE. pp 105–116

  30. Knight K, Chander I, Haines M, Hatzivassiloglou V, Hovy E, Iida M, Luk SK, Whitney R, Yamada K (1995) Filling knowledge gaps in a broad-coverage machine translation system. arXiv preprint cmp-lg/9506009

  31. Knight K, Luk SK (1994) Building a large-scale knowledge base for machine translation. In: AAAI, pp 773–778

  32. Le QT, Lee DY, Park CS (2014) A social network system for sharing construction safety and health knowledge. Autom Constr 46:30–37

    Article  Google Scholar 

  33. Le QT, Pedro A, Park CS (2015) A social virtual reality based construction safety education system for experiential learning. J Intell Robot Syst 79:487–506

    Article  Google Scholar 

  34. Leu SS, Chang CM (2013) Bayesian-network-based safety risk assessment for steel construction projects. Acc Anal Prev 54:122–133

    Article  Google Scholar 

  35. Liu M, Huang R, Xu F (2023) Research on the construction of safety information ontology knowledge base and accident reasoning for complex hazardous production systems-taking methanol production process as an example. Sustainability 15:2568

    Article  Google Scholar 

  36. Liu X, Gao J, He X, Deng L, Duh K, Wang YY (2015) Representation learning using multi-task deep neural networks for semantic classification and information retrieval

  37. Lu Y, Li Q, Zhou Z, Deng Y (2015) Ontology-based knowledge modeling for automated construction safety checking. Saf Sci 79:11–18

    Article  Google Scholar 

  38. Luo G (2009) Design and evaluation of the imed intelligent medical search engine. In: 2009 IEEE 25th international conference on data engineering, IEEE, pp 1379–1390

  39. Macêdo JB, das Chagas Moura M, Aichele D, Lins ID (2022) Identification of risk features using text mining and bert-based models: application to an oil refinery. Process Saf Environ Prot 158:382–399

  40. Mohamed E, Seresht NG, AbouRizk S (2023) Context-driven ontology-based risk identification for onshore wind farm projects: a domain-specific approach. Adv Eng Inform 56:101962

    Article  Google Scholar 

  41. Musen MA (2015) The protégé project: a look back and a look forward. AI Matters 1:4–12

    Article  Google Scholar 

  42. Noy NF, Fergerson RW, Musen MA (2000) The knowledge model of protege-2000: combining interoperability and flexibility. In: Knowledge engineering and knowledge management methods, models, and tools: 12th international conference, EKAW 2000 Juan-les-Pins, France, October 2–6, 2000 Proceedings 12, Springer, pp 17–32

  43. Oberle D (2014) How ontologies benefit enterprise applications. Semantic Web 5:473–491

    Article  Google Scholar 

  44. Park CS, Kim HJ (2013) A framework for construction safety management and visualization system. Autom Constr 33:95–103

    Article  Google Scholar 

  45. Park M, Lee, KW, Lee HS, Jiayi P, Yu J (2013) Ontology-based construction knowledge retrieval system. KSCE J Civ Eng 17:1654–1663

  46. Pedro A, Baik S, Jo J, Lee D, Hussain R, Park C (2023) A linked data and ontology-based framework for enhanced sharing of safety training materials in the construction industry. IEEE Access

  47. Pedro A, Pham-Hang AT, Nguyen PT, Pham HC (2022) Data-driven construction safety information sharing system based on linked data, ontologies, and knowledge graph technologies. Int J Environ Res Public Health 19:794

    Article  Google Scholar 

  48. Peraketh B, Menzel C, Mayer RJ, Fillion F, Futrell MT, DeWitte P et al (1994) Ontology capture method (idef5). Knowledge Based Systems, Incorporated Technical report

  49. Phengsuwan J, Shah T, Sun R, James P, Thakker D, Ranjan R (2022) An ontology-based system for discovering landslide-induced emergencies in electrical grid. Trans Emerg Telecommun Technol 33:e3899

    Article  Google Scholar 

  50. Pinto A (2014) Qram a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets. Saf Sci 63:57–76

    Article  Google Scholar 

  51. Pulido J, Herrera R, Arechiga M, Block A, Acosta R, Legrand S (2006) Identifying ontology components from digital archives for the semantic web. In: IASTED advances in computer science and technology (ACST), pp 1–6

  52. Qiu Q, Xie Z, Zhang D, Ma K, Tao L, Tan Y, Zhang Z, Jiang B (2023) Knowledge graph for identifying geological disasters by integrating computer vision with ontology. J Earth Sci 34:1418–1432

    Article  Google Scholar 

  53. Shen Q, Wu S, Deng Y, Deng H, Cheng JC (2022) Bim-based dynamic construction safety rule checking using ontology and natural language processing. Buildings 12:564

    Article  Google Scholar 

  54. Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering: principles and methods. Data Knowl Eng 25:161–197

    Article  Google Scholar 

  55. Swartout B, Patil R, Knight K, Russ T (1996) Toward distributed use of large-scale ontologies. In: Proceedings of the tenth workshop on knowledge acquisition for knowledge-based systems, p 25

  56. Teizer J, Johansen K, Schultz C (2022) The concept of digital twin for construction safety. Constr Res Congress 2022:1156–1165

    Google Scholar 

  57. Uschold M, Gruninger M (1996) Ontologies: principles, methods and applications. Knowl Eng Rev 11:93–136

    Article  Google Scholar 

  58. Wachter JK, Yorio PL (2014) A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation. Acc Anal Prev 68:117–130

    Article  Google Scholar 

  59. Wang Q, Yu X (2014) Ontology based automatic feature recognition framework. Comput Ind 65:1041–1052

  60. Wang H, Zhang F, Xie X, Guo M (2018) Dkn: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844

  61. Wang WC, Liu JJ, Chou SC (2006) Simulation-based safety evaluation model integrated with network schedule. Autom Constr 15:341–354

    Article  Google Scholar 

  62. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence, pp 1112–1119

  63. Wu H, Zhong B, Li H, Love P, Pan X, Zhao N (2021) Combining computer vision with semantic reasoning for on-site safety management in construction. J Build Eng 42:103036

    Article  Google Scholar 

  64. Wu H, Zhong B, Medjdoub B, Xing X, Jiao L (2020) An ontological metro accident case retrieval using CBR and NLP. Appl Sci 10:5298

    Article  Google Scholar 

  65. Wu L, Rao Y, Yu H, Wang Y, Ambreen N (2019) A multi-semantics classification method based on deep learning for incredible messages on social media. Chin J Electron 28:754–763

    Article  Google Scholar 

  66. Xing X, Zhong B, Luo H, Li H, Wu H (2019) Ontology for safety risk identification in metro construction. Comput Ind 109:14–30

    Article  Google Scholar 

  67. Xu Q, Xu K (2021) Analysis of the characteristics of fatal accidents in the construction industry in China based on statistical data. Int J Environ Res Public Health 18:2162

    Article  Google Scholar 

  68. Ye Y, Yang D, Jiang Z, Tong L (2008) Ontology-based semantic models for supply chain management. Int J Adv Manuf Technol 37:1250–1260

    Article  Google Scholar 

  69. Zhang S, Boukamp F, Teizer J (2014) Ontology-based semantic modeling of safety management knowledge. In: Computing in civil and building engineering, pp 2254–2262

  70. Zhang S, Boukamp F, Teizer J (2015) Ontology-based semantic modeling of construction safety knowledge: towards automated safety planning for job hazard analysis (jha). Autom Constr 52:29–41

    Article  Google Scholar 

  71. Zhao L, Ichise R (2014) Ontology integration for linked data. J Data Semant 3:237–254

    Article  Google Scholar 

  72. Zhu YL (2013) The construction safety accident emergency decision support system based on ontology and CBR. Appl Mech Mater 423:2149–2153

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Humanity and Social Science Research Project of Anhui Provincial Education Department in 2022 (2022AH050224) and Housing Urban and Rural Construction Science and Technology Plan Project of Anhui Province in 2022 (2022-YF082).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei, China

    Donghui Shi & Zhigang Li

  2. Department of Information Systems, Analytics and Operations, College of Business, University of Louisville, Louisville, USA

    Jozef Zurada, Andrew Manikas & Jian Guan

  3. Department of Software Engineering, Gdansk University of Technology, Gdansk, Poland

    Pawel Weichbroth

Authors
  1. Donghui Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhigang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jozef Zurada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Manikas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pawel Weichbroth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jozef Zurada.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, D., Li, Z., Zurada, J. et al. Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents. Knowl Inf Syst 66, 2651–2681 (2024). https://doi.org/10.1007/s10115-023-02036-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-02036-9

Keywords

Search

Navigation