Log in

Predicting Construction Accident Outcomes Using Graph Convolutional and Dual-Edge Safety Networks

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

To improve site safety status, the construction safety literature has investigated machine learning (ML) prediction models with a particular emphasis on their prediction accuracy. This study shifts its focus on reliability of a construction safety model beyond their prediction accuracy by increasing its representativeness. A novel dual-edge construction safety network was synthesized that considers the mutual contribution of behaviors (human factors) and the physical environment (workplace factors). A graph convolutional network (GCN) was created to learn the high-level information of the dual-edge network to predict the severity outcome of construction accidents. The dual-edge GCN model was tested on a comprehensive construction safety dataset collected from 73 projects that resulted in an 85.67% prediction accuracy while leveraging shared human and workplace factors in predicting construction accident outcomes. The incorporated dual-edge safety network offers more representative and explainable accident visualization that enables prioritizing related safety interventions and develo** tailored prevention strategies based on two different decision objectives. Compared with other ML approaches, the proposed construction safety model emphasizes both human and workplace factors without trading off its prediction accuracy, thereby increasing the reliability of the prediction outcome for integration in relevant safety decisions. The transparency of the input network and its accident visualization enable practitioners to develop tailored prevention strategies while increasing trust in accident prediction outcomes.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

Some of the data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Mostofi, F.; Toğan, V.: Construction safety predictions with multi-head attention graph and sparse accident networks. Autom. Constr. 156, 105102 (2023). https://doi.org/10.1016/j.autcon.2023.105102

    Article  Google Scholar 

  2. Albert, A.; Pandit, B.; Patil, Y.; Louis, J.: Does the potential safety risk affect whether particular construction hazards are recognized or not? J. Saf. Res. 75, 241–250 (2020). https://doi.org/10.1016/J.JSR.2020.10.004

    Article  Google Scholar 

  3. Sanni-Anibire, M.O.; Mahmoud, A.S.; Hassanain, M.A.; Salami, B.A.: A risk assessment approach for enhancing construction safety performance. Saf. Sci. 121, 15–29 (2020). https://doi.org/10.1016/J.SSCI.2019.08.044

    Article  Google Scholar 

  4. Mostofi, F.; Toğan, V.: A data-driven recommendation system for construction safety risk assessment. J. Constr. Eng. Manag. (2023). https://doi.org/10.1061/JCEMD4.COENG-13437

    Article  Google Scholar 

  5. Mammadov, A.; Kazar, G.; Koc, K.; Tokdemir, O.B.: Predicting accident outcomes in cross-border pipeline construction projects using machine learning algorithms. Arab. J. Sci. Eng. (2023). https://doi.org/10.1007/s13369-023-07964-w

    Article  Google Scholar 

  6. Liu, Q.; Chen, Z.: Early warning control model and simulation study of engineering safety risk based on a convolutional neural network. Neural Comput. Appl. (2023). https://doi.org/10.1007/S00521-022-08170-9/FIGURES/4

    Article  Google Scholar 

  7. Karimiazari, A.; Mousavi, N.; Mousavi, S.F.; Hosseini, S.: Risk assessment model selection in construction industry. Expert Syst. Appl. 38, 9105–9111 (2011). https://doi.org/10.1016/J.ESWA.2010.12.110

    Article  Google Scholar 

  8. Hegde, J.; Rokseth, B.: Applications of machine learning methods for engineering risk assessment: a review. Saf. Sci. 122, 104492 (2020). https://doi.org/10.1016/j.ssci.2019.09.015

    Article  Google Scholar 

  9. Pan, Y.; Zhang, L.: Roles of artificial intelligence in construction engineering and management: a critical review and future trends. Autom. Constr. 122, 103517 (2021). https://doi.org/10.1016/j.autcon.2020.103517

    Article  Google Scholar 

  10. Zhu, R.; Hu, X.; Hou, J.; Li, X.: Application of machine learning techniques for predicting the consequences of construction accidents in China. Process. Saf. Environ. Prot. 145, 293–302 (2021). https://doi.org/10.1016/j.psep.2020.08.006

    Article  Google Scholar 

  11. Mostofi, F.; Toğan, V.: Explainable safety risk management in construction with unsupervised learning. Presented at the March 17 (2023)

  12. Mostofi, F.; Toğan, V.; Ayözen, Y.E.; Tokdemir, O.B.: Construction safety risk model with construction accident network: a graph convolutional network approach. Sustainability 14, 15906 (2022). https://doi.org/10.3390/su142315906

    Article  Google Scholar 

  13. **, R.; Zou, P.X.W.; Piroozfar, P.; Wood, H.; Yang, Y.; Yan, L.; Han, Y.: A science map** approach based review of construction safety research. Saf. Sci. 113, 285–297 (2019). https://doi.org/10.1016/J.SSCI.2018.12.006

    Article  Google Scholar 

  14. Ahmadi, M.; Kioumarsi, M.: Predicting the elastic modulus of normal and high strength concretes using hybrid ANN-PSO. Mater. Today Proc. (2023). https://doi.org/10.1016/j.matpr.2023.03.178

    Article  Google Scholar 

  15. Ahmadi, M.; Kheyroddin, A.; Dalvand, A.; Kioumarsi, M.: New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams. Constr. Build. Mater. 234, 117293 (2020). https://doi.org/10.1016/j.conbuildmat.2019.117293

    Article  Google Scholar 

  16. Mostofi, S.; Yesevi Okur, F.; Altunışık, A.C.: Fire assessment of suspension bridge towers: A machine learning-based prediction of AST under varying vehicle fire conditions. In: Proceedings of 3rd International Civil Engineering and Architecture Congress (ICEARC’23). pp. 1819–1827. Golden light publishing, Trabzon (2023)

  17. Mostofi, F.; Toğan, V.; Tokdemir, O.B.: Enhancing construction productivity prediction through variational autoencoders and graph attention network. In: Proceedings of 3rd International Civil Engineering and Architecture Congress (ICEARC’23), pp. 120–128. Golden light Publishing, Trabzon (2023)

  18. Yin, H.; Wu, Q.; Yin, S.; Dong, S.; Dai, Z.; Soltanian, M.R.: Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest. J. Hydrol. 616, 128813 (2023). https://doi.org/10.1016/j.jhydrol.2022.128813

    Article  Google Scholar 

  19. Oguz Erkal, E.D.; Hallowell, M.R.; Bhandari, S.: Practical assessment of potential predictors of serious injuries and fatalities in construction. J. Constr. Eng. Manag. 147, 04021129 (2021). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002146

    Article  Google Scholar 

  20. Goh, Y.M.; Chua, D.: Neural network analysis of construction safety management systems: a case study in Singapore. Constr. Manag. Econ. 31, 460–470 (2013). https://doi.org/10.1080/01446193.2013.797095

    Article  Google Scholar 

  21. Tixier, A.J.P.; Hallowell, M.R.; Rajagopalan, B.; Bowman, D.: Application of machine learning to construction injury prediction. Autom. Constr. 69, 102–114 (2016). https://doi.org/10.1016/j.autcon.2016.05.016

    Article  Google Scholar 

  22. Ayhan, B.U.; Tokdemir, O.B.: Safety assessment in megaprojects using artificial intelligence. Saf. Sci. 118, 273–287 (2019). https://doi.org/10.1016/j.ssci.2019.05.027

    Article  Google Scholar 

  23. Ayhan, B.U.; Tokdemir, O.B.: Predicting the outcome of construction incidents. Saf. Sci. 113, 91–104 (2019). https://doi.org/10.1016/j.ssci.2018.11.001

    Article  Google Scholar 

  24. Ayhan, B.U.; Tokdemir, O.B.: Accident analysis for construction safety using latent class clustering and artificial neural networks. J. Constr. Eng. Manag. 146, 04019114 (2020). https://doi.org/10.1061/(asce)co.1943-7862.0001762

    Article  Google Scholar 

  25. Mostofi, F.; Toğan, V.: Construction safety hazard recommendation using graph representation learning. In: 7th International Project and Construction Management Conference (IPCMC 2022), pp. 1376–1386. PCMC 2022, Istanbul (2022)

  26. Yan, X.; Zhang, H.; Li, H.: Computer vision-based recognition of 3D relationship between construction entities for monitoring struck-by accidents. Comput. Aided Civ. Infrastruct. Eng. 35, 1023–1038 (2020). https://doi.org/10.1111/MICE.12536

    Article  Google Scholar 

  27. Smith, L.N.; Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. 36 (2019). https://doi.org/10.1117/12.2520589

  28. Chen, F.: Safety evaluation method of hoisting machinery based on neural network. Neural Comput. Appl. 33, 565–576 (2021). https://doi.org/10.1007/S00521-020-04963-Y/FIGURES/8

    Article  Google Scholar 

  29. Piao, Y.; Xu, W.; Wang, T.-K.; Chen, J.-H.: Dynamic fall risk assessment framework for construction workers based on dynamic Bayesian network and computer vision. J. Constr. Eng. Manag. (2021). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002200

    Article  Google Scholar 

  30. Mistikoglu, G.; Gerek, I.H.; Erdis, E.; Mumtaz Usmen, P.E.; Cakan, H.; Kazan, E.E.: Decision tree analysis of construction fall accidents involving roofers. Expert Syst. Appl. 42, 2256–2263 (2015). https://doi.org/10.1016/j.eswa.2014.10.009

    Article  Google Scholar 

  31. Goh, Y.M.; Binte Sa’adon, N.F.: Cognitive factors influencing safety behavior at height: a multimethod exploratory study. J. Constr. Eng. Manag. 141, 04015003 (2015). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000972

    Article  Google Scholar 

  32. Ma, Y.; Chowdhury, M.; Sadek, A.; Jeihani, M.: Real-time highway traffic condition assessment framework using vehicle-infrastructure integration (VII) with artificial intelligence (AI). IEEE Trans. Intell. Transp. Syst. 10, 615–627 (2009). https://doi.org/10.1109/TITS.2009.2026673

    Article  Google Scholar 

  33. Ding, C.; Wu, X.; Yu, G.; Wang, Y.: A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data. Transp. Res. Part C Emerg. Technol. 72, 225–238 (2016). https://doi.org/10.1016/j.trc.2016.09.016

    Article  Google Scholar 

  34. Zhu, M.; Li, Y.; Wang, Y.: Design and experiment verification of a novel analysis framework for recognition of driver injury patterns: from a multi-class classification perspective. Accid. Anal. Prev. 120, 152–164 (2018). https://doi.org/10.1016/j.aap.2018.08.011

    Article  Google Scholar 

  35. Grande, Z.; Castillo, E.; Mora, E.; Lo, H.K.: Highway and road probabilistic safety assessment based on Bayesian network models. Comput. Aided Civ. Infrastruct. Eng. 32, 379–396 (2017). https://doi.org/10.1111/MICE.12248

    Article  Google Scholar 

  36. Farid, A.; Abdel-Aty, M.; Lee, J.: A new approach for calibrating safety performance functions. Accid. Anal. Prev. 119, 188–194 (2018). https://doi.org/10.1016/j.aap.2018.07.023

    Article  Google Scholar 

  37. Toğan, V.; Mostofi, F.; Ayözen, Y.; Behzat Tokdemir, O.: Customized AutoML: an automated machine learning system for predicting severity of construction accidents. Buildings 12, 1933 (2022). https://doi.org/10.3390/buildings12111933

    Article  Google Scholar 

  38. Al-Ghamdi, A.S.: Using logistic regression to estimate the influence of accident factors on accident severity. Accid. Anal. Prev. 34, 729–741 (2002). https://doi.org/10.1016/S0001-4575(01)00073-2

    Article  Google Scholar 

  39. Sugumaran, V.; AjithKumar, R.; Gowda, B.H.L.; Sohn, C.H.: Safety analysis on a vibrating prismatic body: a data-mining approach. Expert Syst. Appl. 36, 6605–6612 (2009). https://doi.org/10.1016/j.eswa.2008.08.041

    Article  Google Scholar 

  40. Kwon, O.H.; Rhee, W.; Yoon, Y.: Application of classification algorithms for analysis of road safety risk factor dependencies. Accid. Anal. Prev. 75, 1–15 (2015). https://doi.org/10.1016/j.aap.2014.11.005

    Article  Google Scholar 

  41. Liang, Y.; Reyes, M.L.; Lee, J.D.: Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans. Intell. Transp. Syst. 8, 340–350 (2007). https://doi.org/10.1109/TITS.2007.895298

    Article  Google Scholar 

  42. Tango, F.; Botta, M.: Real-time detection system of driver distraction using machine learning. IEEE Trans. Intell. Transp. Syst. 14, 894–905 (2013). https://doi.org/10.1109/TITS.2013.2247760

    Article  Google Scholar 

  43. Farid, A.; Abdel-Aty, M.; Lee, J.: Comparative analysis of multiple techniques for develo** and transferring safety performance functions. Accid. Anal. Prev. 122, 85–98 (2019). https://doi.org/10.1016/j.aap.2018.09.024

    Article  Google Scholar 

  44. Koc, K.; Ekmekcioğlu, Ö.; Gurgun, A.P.: Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Autom. Constr. 131, 103896 (2021). https://doi.org/10.1016/j.autcon.2021.103896

    Article  Google Scholar 

  45. Salarian, A.A.; Etemadfard, H.; Rahimzadegan, A.; Ghalehnovi, M.: Investigating the role of clustering in construction-accident severity prediction using a heterogeneous and imbalanced data set. J. Constr. Eng. Manag. 149, 04022161 (2022). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002406

    Article  Google Scholar 

  46. Li, P.; Li, K.; Wang, F.; Zhang, Z.; Cai, S.; Cheng, L.: A novel method for gas disaster prevention during the construction period in coal penetration tunnels—a stepwise prediction of gas concentration based on the LSTM method. Sustainability 14, 12998 (2022). https://doi.org/10.3390/su142012998

    Article  Google Scholar 

  47. Mostofi, F.; Tokdemir, O.B.; Toğan, V.: Comprehensive root cause analysis of construction defects using semisupervised graph representation learning. J. Constr. Eng. Manag. (2023). https://doi.org/10.1061/jcemd4.coeng-13435

    Article  Google Scholar 

  48. Zhang, Y.; Li, Y.; Kong, Y.; Wu, J.; Yang, J.; Shu, H.; Coatrieux, G.: GSCFN: a graph self-construction and fusion network for semi-supervised brain tissue segmentation in MRI. Neurocomputing 455, 23–37 (2021). https://doi.org/10.1016/J.NEUCOM.2021.05.047

    Article  Google Scholar 

  49. Tian, D.; Li, M.; Han, S.; Shen, Y.: A novel and intelligent safety-hazard classification method with syntactic and semantic features for large-scale construction projects. J. Constr. Eng. Manag. (2022). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002382

    Article  Google Scholar 

  50. Pan, X.; Zhong, B.; Wang, Y.; Shen, L.: Identification of accident-injury type and bodypart factors from construction accident reports: a graph-based deep learning framework. Adv. Eng. Inform. (2022). https://doi.org/10.1016/J.AEI.2022.101752

    Article  Google Scholar 

  51. Ribeiro, M.T.; Singh, S.; Guestrin, C.: Why should I trust You?”: explaining the predictions of any classifier. In: NAACL-HLT 2016—2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session, pp. 97–101 (2016). https://doi.org/10.18653/V1/N16-3020

  52. Chasalow, K.; Levy, K.: Representativeness in statistics, politics, and machine learning. In: FAccT 2021—Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 77–89 (2021). https://doi.org/10.1145/3442188.3445872

  53. Karimi, H.; Asce, A.M.; Taghaddos, H.; Eng, P.; Asce, M.: Impact of age on the strength of experience and education role in fatal injuries prevention in iranian construction craft workers. J. Constr. Eng. Manag. 146, 04020070 (2020). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001857

    Article  Google Scholar 

  54. Dong, X.S.; Fujimoto, A.; Ringen, K.; Men, Y.: Fatal falls among Hispanic construction workers. Accid. Anal. Prev. 41, 1047–1052 (2009). https://doi.org/10.1016/J.AAP.2009.06.012

    Article  Google Scholar 

  55. Amissah, J.; Badu, E.; Agyei-Baffour, P.; Nakua, E.K.; Mensah, I.: Predisposing factors influencing occupational injury among frontline building construction workers in Ghana. BMC. Res. Notes 12, 1–8 (2019). https://doi.org/10.1186/S13104-019-4744-8/TABLES/3

    Article  Google Scholar 

  56. Salama, K.: Node classification with graph neural networks. https://keras.io/examples/graph/gnn_citations/

  57. Qu, Z.; Liu, X.; Zheng, M.: Temporal–spatial quantum graph convolutional neural network based on schrödinger approach for traffic congestion prediction. IEEE Trans. Intell. Transp. Syst. 24, 8677–8686 (2023). https://doi.org/10.1109/TITS.2022.3203791

    Article  Google Scholar 

  58. Liu, Y.; Zhang, X.; Zhou, J.; Fu, L.: SG-DSN: a semantic graph-based dual-stream network for facial expression recognition. Neurocomputing 462, 320–330 (2021). https://doi.org/10.1016/J.NEUCOM.2021.07.017

    Article  Google Scholar 

  59. Kipf, T.N.; Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017—Conference Track Proceedings, pp. 1–14 (2017)

  60. Chen, L.; **e, Y.; Zheng, Z.; Zheng, H.; **e, J.: Friend recommendation based on multi-social graph convolutional network. IEEE Access 8, 43618–43629 (2020). https://doi.org/10.1109/ACCESS.2020.2977407

    Article  Google Scholar 

  61. Wu, L.; Sun, P.; Hong, R.; Fu, Y.; Wang, X.; Wang, M.: SocialGCN: an efficient graph convolutional network based model for social recommendation (2018). https://doi.org/10.48550/arxiv.1811.02815

  62. Liao, R.; Zhao, Z.; Urtasun, R.; Zemel, R.S.: LanczosNet: multi-scale deep graph convolutional networks. In: 7th International Conference on Learning Representations, ICLR 2019, pp. 1–18 (2019)

  63. Abu-El-Haija, S.; Kapoor, A.; Perozzi, B.; Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019 (2019)

  64. Gao, L.; Lu, P.; Ren, Y.: A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents. Reliab. Eng. Syst. Saf. 216, 108019 (2021). https://doi.org/10.1016/j.ress.2021.108019

    Article  Google Scholar 

  65. Chandar, S.; Reddy, A.; Mansoor, M.; Jamadagni, S.: Road accident proneness indicator based on time, weather and location specificity using graph neural networks. In: Proceedings—19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, pp. 1527–1533. Institute of Electrical and Electronics Engineers Inc. (2020)

  66. Zhang, Y.; Dong, X.; Shang, L.; Zhang, D.; Wang, D.: A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing. In: Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, pp. 1–9. IEEE (2020)

  67. Kipf, T.N.; Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017—Conference Track Proceedings, pp. 1–14 (2017). https://doi.org/10.48550/arxiv.1609.02907

  68. Wu, L.: Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore (2022)

    Book  Google Scholar 

  69. Albert, A.; Hallowell, M.R.; Kleiner, B.; Chen, A.; Golparvar-Fard, M.: Enhancing construction hazard recognition with high-fidelity augmented virtuality. J. Constr. Eng. Manag. 140, 04014024 (2014). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000860

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vedat Toğan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Mostofi, F., Toğan, V. Predicting Construction Accident Outcomes Using Graph Convolutional and Dual-Edge Safety Networks. Arab J Sci Eng (2023). https://doi.org/10.1007/s13369-023-08609-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13369-023-08609-8

Keywords

Navigation