To investigate the seismic performance of a wind turbine that is influenced by both the ice load and the seismic load, the research proposes a numerical approach for simulating the seismic behavior of a wind turbine on a monopile foundation. First, the fluid–solid coupled equation for the water–ice–wind turbine is simplified by assigning reasonable boundary conditions and solving the motion equation, and the seismic motion equation of the wind turbine is developed. Then, on this basis, we propose a simplified 3D numerical model that can simulate the interactions among the wind turbine, water and sea ice. By conducting shaking table tests, the results demonstrate that the established numerical model is effective. Finally, we investigate the effect of the boundary range and ice thickness on the seismic performance of a turbine under near-field and far-field seismic actions. Research results illustrate that ice changes the distribution form of the hydrodynamic pressure. Moreover, the thickness of the ice greatly influences the seismic behavior, while the influence of the ice boundary range is only within a certain range. Additionally, the ice load decreases the energy-dissipating capacity of the wind turbine, so the earthquake resilience of the wind turbine is significantly decreased.

The prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve protection of endangered residential areas and infrastructure. Recently, the K-nearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pattern classification. Therefore the aim of this study was to use K-nearest neighbor (KNN) algorithm to predict the rockfall runout range which is classified into different subintervals according to the distance from the slope toe. First, we proposed the prediction model of the rockfall runout range based on our improved KNN algorithm which could better offer robustness against different choices of the neighborhood size k, and it is the first work of applying our improved KNN algorithm to rockfall runout range prediction. Second, shaking table tests of rockfall runout models were conducted for simulating the rockfall process, and the influence laws of factors-including types of earthquake, peak ground acceleration, vibration frequency, slope angle, slope height, block mass and block shape-on rockfall distance are investigated. Finally, there is a discussion of the performance of our proposed prediction model based on our improved KNN algorithm in the prediction of rockfall runout range. Extensive experimental results for rockfall runout range prediction demonstrate the effectiveness of our proposed prediction model.

An accurate slope prediction model is important for slope reinforcement before the disaster. The k‐nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k‐nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k‐nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.