No CrossRef data available.
Article contents
A machine learning analysis of the impact of helideck surface conditions on helicopter safety
Published online by Cambridge University Press: 29 July 2025
Abstract
This study provides a comprehensive analysis of the impact of helideck surface conditions on the safe operation of helicopter landing and take-off platforms on offshore drilling vessels. Over time, the deterioration of helideck surface coatings necessitates periodic friction coefficient testing every two years in compliance with international standards. Surface coatings that fail to meet the required thresholds are replaced, and the performance of the renewed surface is reassessed using the Helideck Micro GripTester (HMGT), in accordance with U.K. Safety Regulation Group CAP 437 (2023) standards for offshore helicopter landing areas. The findings indicate that the renewed helideck surface coatings lead to a significant increase in the coefficient of friction, thereby enhancing the stability of helicopters upon landing and while on deck. Independent sample t-test and correlation analyses confirmed statistically significant differences between the old and new surface conditions, demonstrating the positive impact of surface improvements on coefficient of friction and, therefore, operational safety. Furthermore, machine learning techniques were employed to model and analyse the non-linear relationships between surface conditions and flow number. The model results demonstrate that variations in helideck surface coatings directly influence helicopter performance and operational safety. These findings underscore the critical importance of regular resurfacing and friction testing in ensuring the safety and reliability of offshore helicopter operations.
Keywords
Information
- Type
- Research Article
- Information
- Copyright
- © The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society
References
Park, J.S., Kishore, R.K., Ha, Y.S. and Jang, K.B. Structural engineering of aluminum helideck structure based on the NORSOK requirement. In International Conference on Offshore Mechanics and Arctic Engineering, American Society of Mechanical Engineers, 2016, 49941, p V003T02A082.10.1115/OMAE2016-55062CrossRefGoogle Scholar
Truelock, D., Czaban, Z., Luo, H., Wang, X., Holtmann, M., Begovic, E., Yasuda, A, Ventura, M., Nicholls-Lee, R., Oterkus, E. and Sensharma, P. Committee V. 5: Special craft, Proceedings of the 20th International Ship and Offshore Structures Congress (ISSC 2018), IOS Press, 2018, 2, pp 279–345.10.3233/978-1-61499-864-8-279CrossRefGoogle Scholar
NORSOK Standard. C-004 Helicopter Deck on Offshore Installations. Standards Norway, 2004, Oslo.Google Scholar
UK Civil Aviation Authority. Standards for Offshore Helicopter Landing Areas (CAP 437) (9th ed.). Civil Aviation Authority, 2023, London. https://www.caa.co.uk/publication/download/12321Google Scholar
ICAO. Annex 14 Aerodromes, Volume II: Heliports (5th ed.). International Civil Aviation Organization, 2020, Montreal.Google Scholar
Oster, C.V. Jr, Strong, J.S. and Zorn, C.K. Analyzing aviation safety: Problems, challenges, opportunities, Res. Transp. Econ., 2013, 43, (1), pp 148–164.10.1016/j.retrec.2012.12.001CrossRefGoogle Scholar
Wilke, S., Majumdar, A. and Ochieng, W.Y. Airport surface operations: A holistic framework for operations modeling and risk management, Saf. Sci., 2014, 63, pp 18–33.10.1016/j.ssci.2013.10.015CrossRefGoogle Scholar
Malakis, S., Kontogiannis, T. and Smoker, A. A pragmatic approach to the limitations of safety management systems in aviation, Saf. Sci., 2023, 166, p 106215.10.1016/j.ssci.2023.106215CrossRefGoogle Scholar
Shyur, H.J. A quantitative model for aviation safety risk assessment, Comput. Ind. Eng., 2008, 54, (1), pp 34–44.10.1016/j.cie.2007.06.032CrossRefGoogle Scholar
Mendes, N., Vieira, J.G.V. and Mano, A.P. Risk management in aviation maintenance: A systematic literature review, Safety Sci., 2022, 153, p 105810.10.1016/j.ssci.2022.105810CrossRefGoogle Scholar
Park, C.S., Chung, M.K., Song, E.H., Baek, K.K. and Chung, E.S. Optimization of application procedure for non-skid coating materials in offshore structure. In NACE CORROSION, NACE, 2008, pp NACE–08022.10.5006/C2008-08022CrossRefGoogle Scholar
Deng, Q., Wang, T.L., Bai, Y. and Jiang, X.J. Preparation of highly wear-resistant non-skid deck coating and analysis of its non-skid wear resistance, Chinese. J. Ship Res, 2020, 15, pp 82–88.Google Scholar
Goulet, J.S. Landing helicopter [Photograph]. Goulet Aviation Services, 2014. Retrieved on June 12, 2024. Available: http://gouletaviationservices.com/helicopters.html
Google Scholar
Chang, W.R., Leclercq, S., Lockhart, T.E. and Haslam, R. State of science: Occupational slips, trips and falls on the same level, Ergonomics, 2016, 59, (7), pp 861–883.Google ScholarPubMed
Yam, K.M., Guo, N., Jiang, Z., Li, S. and Zhang, C. Graphene-based heterogeneous catalysis: Role of graphene, Catalysts, 2020, 10, (1), p 53.10.3390/catal10010053CrossRefGoogle Scholar
Khosroshahi, S.F., Masina, M., Antonini, A., Ransley, E., Brownjohn, J.M.W., Dobson, P. and D’Ayala, D. A multidisciplinary computational framework for topology optimisation of offshore helidecks, J. Mar. Sci. Eng., 2022, 10, (9), p 1180.10.3390/jmse10091180CrossRefGoogle Scholar
Jung, T.W., Kim, B.M. and Ha, S.H. A study on lightweight design of cantilever-type helideck using topology design optimization, J. Comput. Struct. Eng. Inst. Korea, 2017, 30, (5), pp 453–460.10.7734/COSEIK.2017.30.5.453CrossRefGoogle Scholar
Onuchukwu, O.C., Onyiliofor, A.O. and Ekong, A.E. Enhancing aviation safety through helideck surface friction management and maintenance for safe helicopter operations, Asian J. Adv. Res. Rep., 2024, 18, (7), pp 239–245.10.9734/ajarr/2024/v18i7698CrossRefGoogle Scholar
Hu, M., Yao, Z., and Wang, X. Graphene-based nanomaterials for catalysis, Ind. Eng. Chem. Res., 2017, 56, (13), pp 3477–3502.10.1021/acs.iecr.6b05048CrossRefGoogle Scholar
Yan, Y., Shin, W.I., Chen, H., Lee, S.M., Manickam, S., Hanson, S., Zhao, H, Lester, E, Wu, T. and Pang, C.H. A recent trend: Application of graphene in catalysis, Carbon Lett., 2021, 31, pp 177–199.10.1007/s42823-020-00200-7CrossRefGoogle Scholar
Ollik, K. and Lieder, M. Review of the application of graphene-based coatings as anticorrosion layers, Coatings, 2020, 10, (9), p 883.10.3390/coatings10090883CrossRefGoogle Scholar
Fauzi, F., Rianjanu, A., Santoso, I. and Triyana, K. Gas and humidity sensing with quartz crystal microbalance (QCM) coated with graphene-based materials–A mini review, Sens. Actuators A: Phys., 2021, 330, p 112837.10.1016/j.sna.2021.112837CrossRefGoogle Scholar
Zeng, X., Peng, Y., Yu, M., Lang, H., Cao, X.A. and Zou, K. Dynamic sliding enhancement on the friction and adhesion of graphene, graphene oxide, and fluorinated graphene, ACS Appl. Mater. Interfaces, 2018, 10, (9), pp 8214–8224.10.1021/acsami.7b19518CrossRefGoogle ScholarPubMed
Zeng, X., Peng, Y., Liu, L. and Lang, H. Dependence of the friction strengthening of graphene on velocity, Nanoscale, 2018, 10, (4), pp 1855–1864.10.1039/C7NR07517KCrossRefGoogle ScholarPubMed
Findley Irvine. Micro GripTester for helidecks [Photograph], 2024. Available: https://www.findlayirvine.com/micro-griptester-helidecks
Google Scholar
Nascimento, F.A.C., Majumdar, A., Ochieng, W.Y. and Schuster, W. Night-time offshore helicopter operations: A survey of risk levels per phase of flight, flying recency requirement and visual approach technique, Aeronaut. J., 2015, 119, (1222), pp 1475–1498.10.1017/S0001924000011362CrossRefGoogle Scholar
Kramer, O. and Kramer, O. K-nearest neighbors. Dimensionality reduction with unsupervised nearest neighbors, 2013, pp 13–23.10.1007/978-3-642-38652-7_2CrossRefGoogle Scholar
Pan, Z., Wang, Y. and Pan, Y. A new locally adaptive k-nearest neighbor algorithm based on discrimination class, Knowl. Based Syst., 2020, 204, p 106185.10.1016/j.knosys.2020.106185CrossRefGoogle Scholar
Loh, W.Y. Classification and regression trees, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 2011, 1, (1), pp 14–23.10.1002/widm.8CrossRefGoogle Scholar
Breiman, L. Classification and Regression Trees. Routledge, 2017, New York.10.1201/9781315139470CrossRefGoogle Scholar
Xu, P., Ji, X., Li, M. and Lu, W. Small data machine learning in materials science, npj Comput. Mater., 2023, 9, (1), p 42.10.1038/s41524-023-01000-zCrossRefGoogle Scholar
Friedman, J.H. Greedy function approximation: A gradient boosting machine, Ann. Stat., 2001, pp 1189–1232.Google Scholar
Breiman, L. Random forests, Mach. Learn., 2001, 45, pp 5–32.10.1023/A:1010933404324CrossRefGoogle Scholar
Sharma, S., Gupta, V. and Mudgal, D. Response surface methodology and machine learning based tensile strength prediction in ultrasonic assisted coating of poly lactic acid bone plates manufactured using fused deposition modeling, Ultrasonics, 2024, 137, p 107204.10.1016/j.ultras.2023.107204CrossRefGoogle ScholarPubMed
Geurts, P., Ernst, D. and Wehenkel, L. Extremely randomized trees, Machine Learning, 2006, 63, pp 3–42.10.1007/s10994-006-6226-1CrossRefGoogle Scholar
Kavrin, D. and Subbotin, S. Bagging-based instance selection for instance-based classification. In CMIS, 2020, pp 769–783.10.32782/cmis/2608-58CrossRefGoogle Scholar
Sarang, P. Ensemble: Bagging and Boosting: Improving Decision Tree Performance by Ensemble Methods. In Thinking Data Science: A Data Science Practitioner’s Guide. Springer International Publishing, 2023, Cham, pp. 97–129.10.1007/978-3-031-02363-7_5CrossRefGoogle Scholar
Breiman, L. Stacked regressions, Machine Learning, 1996, 24, pp 49–64.10.1023/A:1018046112532CrossRefGoogle Scholar