Analysis of SCF parameters in two planar joints in offshore wind turbine support structures

This study explores the relationships between parameters in D-KT joints under compression and tension loads. A total of 432 models with varying dimensions ( d , T , t , l , and θ ) were created using the finite element (FE) software ANSYS for analysis. Data from these models was then used to generate the sample. The collected data underwent a multivariate analysis, utilizing the Pearson correlation coefficient, to evaluate the relationships between the parameters. To minimize the number of variables and groups, three techniques were used: archived data into clusters, hierarchical and non-hierarchical clustering. The research allowed to obtain deeper insights into the performance of DKT joints under varying conditions by employing thorough statistical analysis and classification methods. The analysis revealed a right-skewed, platykurtic distribution (mean: 25.84, SD: 13.37) with moderate spread. Strong correlations were found in bivariate analysis (e.g., τ-SCF: 0.686). Multivariate techniques reduced variables while retaining 80 % data accuracy. Clustering favored k-means for efficiency, and ANOVA highlighted the model's significance with a strong F-statistic. • FE models were generated with ANSYS to create the dataset. • The models were validated using experimental data. • Multivariate analysis of D KT-joints in offshore wind turbine support structures. • Relationships among parameters was evaluated.