Reliable damage detection in operational offshore wind turbines (OWTs) remains challenging due to the inherent non-stationarity of environmental excitations and signal degradation from noise-contaminated partial measurements. To address these limitations, this study proposes a robust damage detection method for OWTs under non-stationary ambient excitations using partial measurements with strong noise resistance. The method is first developed for a scenario with known non-stationary ambient excitations. By reformulating the time-domain equation of motion in terms of non-stationary cross-correlation functions, structural stiffness parameters are estimated using partially measured acceleration responses through the extended Kalman filter (EKF). To account for the more common case of unknown excitations, the method is enhanced via the extended Kalman filter under unknown input (EKF-UI). This improved approach enables the simultaneous identification of the physical parameters of OWTs and unknown non-stationary ambient excitations through the data fusion of partial acceleration and displacement responses. The proposed method is validated through two numerical cases: a frame structure subjected to known non-stationary ground excitation, followed by an OWT tower under unknown non-stationary wind and wave excitations using limited measurements. The numerical results confirm the method’s capability to accurately identify structural damage even under significant noise contamination, demonstrating its practical potential for OWTs’ damage detection applications.

Progressive failure of spoil slopes under extreme rainfall involves multiscale processes, including rainfall infiltration, fine-particle migration, and weakening of the particle skeleton, while their cross-scale evolution remains insufficiently understood. To investigate this mechanism, laboratory model tests were conducted under a rainfall intensity of 108 mm/h with degrees of compaction of 75%, 80%, and 90%. Scanning electron microscopy (SEM) and PFC2D simulations were further employed to examine failure characteristics at the macroscopic, microstructural, and particle scales. The results show that, at 80% compaction, the slope underwent a progressive sequence of fine-particle migration and toe deposition, erosion-gully development, toe undercutting, and intensified shoulder erosion. Pore-water pressure responses also showed clear spatial differences between the crest, middle slope, and toe. As the degree of compaction increased from 75% to 90%, the maximum deposition length, maximum deposition depth, and dry mass of deposited material decreased by 29.8%, 31.0%, and 38.6%, respectively. SEM observations indicate that increasing compaction generally enhances particle contact and structural integrity, thereby reducing local structural disturbance caused by prolonged rainfall. PFC simulations show that particle displacement is mainly concentrated in the shallow slope, particularly near the shoulder and toe, while the number of bond failures increased from 276 at Step 130 to 3488 at the final failure stage. The experiments and SEM observations provide the basis for comparing compaction levels, whereas the PFC results offer a particle-scale interpretation of progressive failure at 80% compaction. The numerical findings do not establish how particle-scale damage varies with compaction.