Abstract: Seabed wind monopiles are usually installed in offshore soft clay layers with poor engineering properties, and are prone to large deflections even destabilization under complex external loads, affecting the normal operation of the wind power system. Among the existing offshore wind monopile stability studies, monitoring and predicting deflection is one of the most costeffective methods. In view of the shortcomings of the traditional monitoring methods and the nonlinearity of monopile deflection changes, this study proposes a new method for monitoring and predicting the deflection of seabed wind monopiles based on Ultra Weak Fiber Bragging Grating (UWFBG) and Machine Learning (ML), and applies it to a case study of seabed wind monopiles in Shandong Peninsula. The continuous strain data along the monopile were successfully obtained by UWFBG, and the maximum deflection angle of the monopile was calculated to be 0.35°; The load influencing factors of top deflection angle such as wind speed, wind direction and tide were analyzed, and it was found that the top deflection angle was positively correlated with the wind speed and negatively correlated with the amplitude of the tides under the prevailing wind direction; The EEMD-PSO-SVR prediction model was established on this basis and successfully predicted the monopile deflection, compared with the measured values, the root-mean-square error and the mean absolute error of the prediction results were 0.0438° and 0.0358°, which verified the accuracy of the proposed prediction model. 

Key words: offshore wind monopile, Ultra-Weak Fibre Bragg Grating (UWFBG), deflection monitoring, Machine Learning (ML);
EEMD-PSO-SVR