Influence of the Competencies of Lifting Crane Specialists on the Probability of Emergencies

Introduction. The operation of lifting cranes is an integral part of the production processes. For the trouble-free operation of these mechanisms, certain knowledge, skills and abilities are required, which should also be possessed by specialists performing organizational and supervisory functions at facilities where such cranes are involved. Here there is an important problem – the lack of a reasonable connection between the level of development of professional competencies and possible emergency situations, as well as various incidents during the operation of lifting cranes. The authors of this study are trying to solve it. Their goal in this regard is to assess the probability of an emergency during the operation of lifting cranes, depending on the level of professional competence of specialists, through the use of neural networks.

Materials and Methods. The competencies of workers in the operation of lifting cranes (knowledge, skills and work responsibilities) provided for by the professional standard «Specialist in the operation of lifting structures» were used as initial data to train neural networks. Based on them, a list of possible incidents was compiled. For the purposes of training, the results of the certification of 200 conditional employees were generated. During the generation, the Monte Carlo method was used, and the data were output to Excel tables. Neural networks were trained in Python 3.10 in the PyCharm development environment. Open libraries Keras and TensorFlow, as well as auxiliary libraries for data representation and processing (Pandas, NumPy, Scikit-learn) were used for neural networks training.

Results. As a result, a tool was obtained – a neural network in the form of executable program code, which makes it possible to assess the probability of emergencies during the operation of lifting cranes by analyzing the degree of proficiency of specialists in professional competencies. It is proposed to implement artificial intelligence technologies based on neural networks in order to assess the knowledge, skills and abilities of specialists of facilities operating lifting cranes, both during the certification of employees and in the course of work.

Discussion and Conclusion. The main result of using neural networks to assess the knowledge of employees of facilities operating lifting cranes is the expected reduction in accidents, which can be ensured by timely identification of incompetent personnel at the stages of primary certification and, most importantly, during periodic tests of knowledge based on an impartial analysis and evaluation of data.

1. Egelskaya EV, Kalanchukova VA. Analysis of the main causes of injuries to personnel in the operation of lifting devices. Young Researcher of the Don. 2021;4:25–29. (In Russ.).

2. Egelskaya EV, Romanenko MYu. Aspects of Application of a Risk-based Spproach to Hazardous Production Facilities. Safety of Technogenic and Natural Systems. 2020;4:45–49. https://doi.org/10.23947/2541-9129-2020-4-45-49

3. Panfilov AV, Deryushev VV, Korotkiy AA. Recommended Safety Systems for Risk-Oriented Approach. Occupational Safety in Industry 2020;5:48–55. https://doi.org/10.24000/0409-2961-2020-5-48-55 (In Russ.).

4. Chislov ON, Lyabakh NN, Kolesnikov MV, et al. Neural Network Investigation of Transport Systems. Transport: science, equipment, management. 2021;10:9–13. https://doi.org/10.36535/0236-1914-2021-10-2 (In Russ.).

5. Turulev RR. Neiroseti v sistemakh korporativnogo upravleniya. In: Trudy II Vserossiiskoi nauchno-prakticheskoi konferentsii "Tsifrovizatsiya: Rossiya i SNG v kontekste global'noi transformatsii". Petrozavodsk: Mezhdunarodnyi tsentr nauchnogo partnerstva "Novaya Nauka"; 2021. P. 7–16. https://doi.org/10.46916/12042021-2-978-5-00174-191-6 (In Russ.).

6. Barisic AF, Barišić JR, Miloloža I. Digital Transformation: Challenges for Human Resources Management. ENTRENOVA — ENTerprise REsearch InNOVAtiony. 2021;7(1):365-375. https://doi.org/10.54820/GTFN9743

7. Haibin Qiu, Shanghong Shi, Tingdi Zhao, et al. Reference to the Safety Engineering Undergraduate Courses to Improve the Subjects and Contents of the Certified Safety Engineer Qualification and Examination System of China. Research Journal of Applied Sciences, Engineering and Technology. 2013;6(18):3320–3323. https://doi.org/10.19026/rjaset.6.3641

8. Kargar V, Jahangiri M, Alimohammadlu M, et al. Risk assessment of mobile crane overturning in Asymmetric Tandem Lifting (ATL) operation based on fuzzy fault tree analysis (FFTA). Results in Engineering. 2022;16:100755. https://doi.org/10.1016/j.rineng.2022.100755

9. Szpytko J, Salgado Duarte Y. Exploitation Efficiency System of Crane based on Risk Managemen. In: Proceedings of International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL. 2020, Vol. 1. P. 24–31. https://doi.org/10.5220/0010123200240031

10. Xing Yong, Li Fan, Sun Ke, et al. Multi-type electric vehicle load prediction based on Monte Carlo simulation. In: The 4th International Conference on Clean Energy and Electrical Systems (CEES 2022). Tokyo, Japan; 2022, Vol. 8, Supplement 10. P. 966–972. https://doi.org/10.1016/j.egyr.2022.05.264

11. Szpytko J, Salgado Duarte Y. Assessing Impacts of Vine-Copula Dependencies: Case Study of a Digital Platform for Overhead Cranes. In: Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL. 2021, P. 187–196. https://doi.org/10.5220/0010709900003062

12. Stroganov Yu, Belov V, Belova N, et al. Analysis of model for assessing the road train movement stability. Journal of Physics: Conference Series. 2021;1889:042051. 10.1088/1742-6596/1889/4/042051

13. Varadaraj A, Wadi Al BM. A Study on Contribution of Digital Human Resource Management towards Organizational Performance. International Journal of Management Science and Business Administration. 2021;7(5):43–51. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.75.1004

14. Saarikko T, Westergren UH, Blomquist T. Digital transformation: Five recommendations for the digitally conscious firm. Business Horizons. 2020;63(6):825–839. https://doi.org/10.1016/j.bushor.2020.07.005

15. Timofeev GO, Roshchin AB, Improving the resistance of captcha tests against automated recognition by convolutional neural networks. Electrosvyaz Magazine. 2022;6:39–45. https://doi.org/10.34832/ELSV.2022.31.6.006 (In Russ.).

16. Epikhin AI, Kondratyev SI, Khekert EV. Predictiuo of multi-dimensijnal non-stationary time series using neuromodeling. Marine Intellectual Technologies. 2020;4(4):23–27 https://doi.org/10.37220/MIT.2020.50.4.092 (In Russ.).

17. Fuqiang Sun, Fangyou Fu, Haitao Liao, et al. Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula. Reliability Engineering and System Safety. 2020;204:107168. https://doi.org/10.1016/j.ress.2020.107168

18. Kraus S, Jones P, Roig-Tierno N, et al. Digital Transformation: An Overview of the Current State of the Art of Research. SAGE Open. 2021;11(3) https://doi.org/10.1177/21582440211047576

19. Spirina A, Mironovа A, Datkhuzheva R, et al Analysis of Occupational Injuries in Construction and Offer of a Technical Solution Increasing Tower Crane Stability. In: XIII International Conference on Transport Infrastructure: Territory Development and Sustainability. — Transportation Research Procedia. 2023, Vol. 68. P. 559–565. https://doi.org/10.1016/j.trpro.2023.02.076

20. Ozkeser B. Impact of training on employee motivation in human resources management. In: 3rd World Conference on Technology, Innovation and Entrepreneurship (WOCTINE). Procedia Computer Science. 2019, Vol. 158. P. 802–810. https://doi.org/10.1016/j.procs.2019.09.117