FLOATBench: A Tabular Dataset and Benchmark for Fatigue Prediction on Floating Offshore Wind Turbine Towers

Tabular surrogates are commonly ranked on random validation splits. Yet a surrogate is used to predict wherever no simulation exists, both between training points (interpolation) and beyond the training domain (extrapolation), and random splits cannot tell the two apart, so the selected model may fail where it extrapolates. We introduce FLOATBench, a tabular dataset and benchmark for fatigue prediction on floating offshore wind turbine towers that tests whether model selection stays valid as predictions move from interpolation to extrapolation. It provides 582,120 per-section fatigue-damage labels from 19,404 OpenFAST simulations of three 22 MW tower geometries over 1,078 wind–wave operating conditions. FLOATBench is distinguished by a regime-aware partition that places each test point relative to the training domain as in-train, interpolation, or extrapolation, so extrapolation depth is controlled by construction, and by cross-tower transfer with condition-level bootstrap intervals. Across 735 trained surrogates and seven standalone baselines on three towers, the global rank-1 model falls to ranks 23, 11, and 11 in joint wind-and-wave extrapolation, where a network ranked 69th–79th globally wins (a rank reversal), and validation-based selection leaves 1.5–1.9× the best available extrapolation error. In cross-tower transfer, the relative L2 error is 0.067–0.098 when the reference tower is in training versus 0.423 when it is held out. The rank reversal holds across all 15 tested partition settings and after retraining on two alternative training grids (REF). Limitations: one 22 MW design family and aligned power-production conditions. Resources: dataset and code at https://anonymous.4open.science/w/FLOATBench-115C/index.html.