The Aeronautical Journal November 2025 Vol 129 No 1341

Helicopter operations on offshore platforms represent one of the most safety-critical aspects of maritime and energy sector logistics. In such environments, even small variations in helideck surface conditions can profoundly influence helicopter performance during landing, take-off, and ground handling. Our recent study, published in The Aeronautical Journal, offers a data-driven exploration of this phenomenon—using machine learning techniques to quantify how helideck surface deterioration and renewal affect operational safety.

Helideck coatings are exposed to some of the harshest conditions imaginable—salt-laden air, high humidity, and mechanical wear from repeated helicopter operations. Over time, these factors reduce surface friction, increasing the risk of skidding and instability. To address this, international standards such as the U.K. Civil Aviation Publication CAP 437 (2023) require regular friction testing and maintenance to ensure that surface conditions meet safety thresholds.

In this study, we analysed empirical data collected from a drillship helideck used in natural gas exploration. Measurements were obtained using the Helideck Micro GripTester (HMGT) before and after resurfacing procedures. The statistical analyses, including independent sample t-tests and correlation tests, revealed significant improvements in the coefficient of friction following surface renewal. These results empirically confirmed that resurfacing not only restores compliance with safety regulations but also enhances the stability of helicopters during landing and on-deck operations.

Beyond conventional statistics, the research leveraged machine learning models to capture non-linear relationships between surface conditions and helicopter performance metrics. The models revealed that even subtle variations in friction coefficients and surface water flow could meaningfully alter flight safety margins. This highlights the capability of machine learning to detect complex interactions that might be overlooked by traditional methods.

Ultimately, our findings emphasise the essential role of proactive maintenance and data-driven monitoring in offshore aviation safety. Regular testing and surface renewal not only extend the operational lifespan of helidecks but also directly contribute to the prevention of accidents. As offshore operations expand and environmental conditions grow more challenging, integrating predictive analytics into maintenance strategies could redefine the future of helicopter safety management.

By combining engineering measurement, statistical validation, and machine learning modelling, this study contributes a novel framework for understanding and enhancing the safety of offshore helicopter operations—where every landing counts.

The paper A machine learning analysis of the impact of helideck surface conditions on helicopter safety by G Çoşkun appears in Volume 129 Issue 1341 of The Aeronautical Journal and is free to access until the end of November 2025.

The Aeronautical Journal has, for over a century, been the UK’s leading scientific and technical aeronautics Journal and is the world’s oldest Aerospace Journal that remains in production. Published monthly, The Aeronautical Journal draws upon the expertise and resources of The Royal Aeronautical Society providing a world-wide forum for authors from the UK and overseas. Research papers are solicited on all aspects of research, design and development, construction and operation of aircraft and space vehicles. Papers are also welcomed which review, comprehensively, the results of recent research developments in any of the above topics.

The Royal Aeronautical Society is the world’s only professional body dedicated to the entire aerospace community. Established in 1866 to further the art, science and engineering of aeronautics, the Society has been at the forefront of developments ever since.

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