Wind and wave actions that vary in amplitude, frequency and direction cause irregular cyclic loading on monopiles supporting offshore wind turbines (OWTs), resulting in cumulative deformation. Current design practice apply widely accepted classification methods to decompose a storm history into an idealised series of cyclic load parcels with uniform amplitude, ordered in magnitude. This approach is based on Miner's rule, which assumes that the final accumulated deformation in the soil is independent of the sequence in which load cycles are applied. Research has shown this approach to be reasonable under drained conditions in sand. This study investigates the validity of this assumption under fully undrained conditions in clay through a series of three dimensional (3D) finite element analyses incorporating an advanced soil constitutive model. A large diameter monopile installed in an overconsolidated clay deposit is subjected to cyclic loading sequences arranged in ascending, descending, and mixed-sorted order. The effect of the load ordering sequence is demonstrated by comparing local soil behaviour in terms of cyclic ratcheting, strain accumulation, clay-structure degradation and excess pore-water pressure buildup and linking these to the global pile response in terms of pile rotation, stiffness, and damping. Findings show that under fully undrained conditions, the ordering of cyclic load sequence notably affects the performance of monopiles in overconsolidated clay deposits. These results suggest that experimental investigations are needed to further explore cyclic loading sequences on monopiles in clay, which could inform the development of improved numerical and design procedures for offshore monopiles.

Structural health monitoring (SHM) is crucial in the early stage of damage formation for the life-cycle service of offshore structures. The influence of soils on vibration-based damage detection systems in offshore structures is a critical issue but has received less attention in previous literature. Due to the complexity of offshore structures and their exposure to diverse loads, simultaneous compound damages across different components can occur, posing a significant challenge for damage detection. Existing methods often treat compound damage as a distinct type of damage, independent of corresponding single damages. Nonetheless, in cases where damages arise concurrently, the distinct characteristics of each individual damage are evident independently within the vibration signals. This study presents a new approach for detecting both single and compound damage in offshore structures considering soil interaction using vibration data. The approach combines Wavelet Transform (WT) with a Multiple Interference Deep Convolutional Neural Network (MIDCNN) to effectively learn desired features and detect damage in these structures. The MIDCNN model is trained on time-frequency data from healthy and single damage states, without incorporating time-frequency data from compound damage during training. In the testing phase, the MIDCNN model intelligently alarms healthy, single damage states, and an untrained compound damage state based on predefined probabilistic conditions derived from the MIDCNN output probabilities. The time-frequency data are generated using the WT method, which is adept at capturing the natural characteristics of the structure while minimizing the influence of noise or irrelevant components. The proposed approach is validated using measured data from a laboratory-scale offshore monopile model with soil interaction. The findings demonstrate that the proposed method is more robust than other methods in extracting features and classifying various states, including healthy, single and compound damages.

The reliability of monopiles is paramount for the uninterrupted operation of offshore wind turbines. However, this reliability is often challenged by environmental factors, such as scour and corrosion, as well as the inherent uncertainty in loads, soil properties, and environmental variables. Therefore, this study proposes a time-variant non-probabilistic reliability assessment strategy for laterally loaded offshore monopiles under scour and corrosion. This strategy is grounded in non-probabilistic interval and interval process models, along with a lateral response analysis of soil-pile system that accounts for the effects of scour and corrosion. The time-variant reliability index is determined using an enhanced HL-RF algorithm. The application of this method to specific cases of laterally loaded monopiles under scour and corrosion demonstrates its remarkable feasibility and adaptability, even for high-dimensional uncertainty scenarios. Furthermore, a sensitivity analysis, based on the proposed strategy, enables the identification of uncertain parameters that significantly impact monopile reliability, providing valuable insights for practical engineering applications. Additionally, this framework has the potential to be extended for a more comprehensive evaluation of the fatigue characteristics of offshore monopiles, an area that merits further exploration.