Abstract
The bearing capacity reliability of monopiles is one fundamental problem in monopile foundation design. In this paper, the response surface method (RSM) based on BP neural network (BPNN) is adopted to study the bearing capacity reliability of monopile-supported OWTs under the serviceability limit state (SLS). The maximum allowed rotation angle of the monopile at the mudline is adopted as the structural failure criteria. The function values at test points of the response surface are determined using the finite element models (FEM) in which the pile-soil interaction effect is considered. The detailed analytical process of the coupled FEM-BPNN-RSM is first introduced. Then the finite element model is constructed regarding the pile-soil interaction, and the accuracy of the finite element model is verified through the comparison with existing studies. Then, a detailed case study is carried out by taking the LW 8 MW OWT planted in Shandong Bozhong wind farm as an example. This case study considers the actual geological conditions and the correlation of environmental parameters of extreme wind speed, wave height and wave period. Different RSMs are adopted, and the bearing capacity reliabilities of monopile-supported OWTs under SLS are obtained and compared.
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