Abstract
Offshore wind turbines are subjected to long-term cyclic loads, and the seabed materials surrounding the foundation are susceptible to failure, which affects the safe construction and normal operation of offshore wind turbines. The existing studies of the cyclic mechanical properties of submarine soils focus on the accumulation strain and liquefaction, and few targeted studies are conducted on the hysteresis loop under cyclic loads. Therefore, 78 representative submarine soil samples from four offshore wind farms are tested in the study, and the cyclic behaviors under different confining pressures and CSR are investigated. The experiments reveal two unique development modes and specify the critical CSR of five submarine soil martials under different testing conductions. Based on the dynamic triaxial test results, the machine learning-based partition models for cyclic development mode were established, and the discrimination accuracy of the hysteresis loop were discussed. This study found that the RF model has a better generalization ability and higher accuracy than the GBDT model in discriminating the hysteresis loop of submarine soil, the RF model has achieved a prediction accuracy of 0.96 and a recall of 0.95 on the test dataset, which provides an important theoretical basis and technical support for the design and construction of offshore wind turbines.