Monopiles are the most commonly used foundation type for offshore wind turbines. Traditionally, monopile foundations were installed using jack-up vessels, which can fix themselves to the seabed using extendable legs. However, as offshore wind farms expand into deeper waters and monopile sizes continue to grow, jack-up vessels are becoming less suitable. To overcome these challenges, SSCV have become viable alternatives. These vessels are equipped with a DP control system, allowing for precise positioning of the vessel without the need for anchors. This advancement significantly improves installation efficiency. However, because these floating vessels are not fixed to the seabed, their movement introduces a new challenge: maintaining the monopile in a vertical position during the installation process while experiencing vessel motions due to waves, currents, and wind. To mitigate this issue, a MCGF can be used.

A major challenge in monopile installation occurs when the tip of the pile contacts the seabed and establishes a connection with the lateral soil. This introduces an abrupt change in the overall system dynamics and increases the risk of DP instability problems of the vessel. These problems are not new to the offshore industry. However, with the introduction of the MCGF, the risk of DP instability increases, as there is now a much stronger dynamic coupling between the vessel, the monopile and the seabed. Therefore, it is important that the MCGF is properly controlled, as this directly influences the reaction forces applied to the vessel. However, properly tuning the MCGF controller is challenging, as it strongly depends on the lateral soil behavior. Currently, these controller gains are tuned using simulations in which the soil behavior is modeled using CPT data. Nevertheless, the estimated behavior based on this data contain large uncertainties and therefore the control settings might become suboptimal during the installation. Furthermore, since soil dynamics also changes during installation, it is beneficial to use a real-time estimation method that takes this into account. Therefore, this thesis investigates different identification methods to obtain real-time estimates of lateral soil behavior during the monopile installation process.

Two main approaches are explored; the augmented EKF approach and the GPLFM approach. The augmented EKF approach shows to be capable of estimating soil behavior by directly identifying the lateral and rotational soil stiffness values, given that the filter is carefully tuned. However, tuning the filter is computationally demanding due to the large number of tunable parameters, which makes this method impractical for site-specific tuning prior to installation using the first measurements obtained. This limitation is critical, as offshore installations face varying conditions at each site, and accurate estimation therefore requires site-specific tuning. To address this tuning challenge, an alternative method is introduced: the GPLFM approach. In this framework, the identification task is reformulated as a GP regression problem. An important advantage of this method is that the process covariance matrix is determined in a data-driven manner, providing a complete covariance structure governed by only a small number of tunable parameters. Consequently, the parameter space is greatly reduced, enabling efficient site-specific tuning. As a result, it is shown that it is possible to obtain accurate estimation results across varying conditions.

Therefore, it is found that the GPLFM method offers a promising solution for real-time estimation of lateral soil behavior during monopile installation. By providing real-time estimates, this study supports the development of more effective MCGF control strategie. This can in the future be used to improve the MCGF controller as it can now be adjusted automatically during the installation process rather than manually. Furthermore, it can help to prevent DP instability problems.