Applications of Data Science in Offshore Geotechnical Engineering: State of Practice and Future Perspectives
Bruno Stuyts, Stephen K. Suryasentana
Abstract
Data-driven predictive models are becoming ubiquitous in society with a wide range of applications, including engineering design. For offshore geotechnical engineering, semi-empirical models make up most of the predictive models to date. These models implicitly include knowledge on soil mechanics and foundation behaviour gathered from laboratory testing, scale model testing and field observations. Modern data science techniques enable researchers and practising engineers to leverage state-of-the-art artificial intelligence tools to re-evaluate the creation of these predictive models. This keynote explores basic and advanced applications of artificial intelligence in offshore geotechnical engineering and aims to offer a perspective on the future use of these techniques in solving complex geotechnical problems.