Bayesian Updating for Prediction of Scour Depth Using Natural Frequency of Monopiles

Abstract

Scour is a non-negligible issue of monopiles that profoundly threatens the safety of monopile for offshore wind turbines (OWTs). Accurately predicting the scour depth is essential for the design and operation of OWTs. This study introduces a model aimed at predicting scour depth from the aspect of the natural frequency of monopile. The model is developed using uniform design samples to ensure its applicability across a wider range of OWT monopiles and soil properties. To enhance the model accuracy, a Bayesian framework is employed, incorporating prior information. The three main model coefficients are updated iteratively, allowing the predicted scour depth to converge with the observed values. The Monte Carlo Markov chain (MCMC) simulation is utilized to generate the posterior distribution. The model accuracy is validated through 48 representative samples, and the effectiveness of Bayesian updating in improving the model precision is demonstrated by comparing the results prior to and following Bayesian updating. Additionally, the numerical simulations and monitored data confirm the validity of the proposed prediction model.