README.md

---
license: cc-by-4.0
size_categories:
- 100K<n<1M
task_categories:
- other
pretty_name: 'FLOATBench: Wind Turbine Tower Damage'
tags:
- floating-offshore-wind-turbine
- tower-fatigue
- 22-MW-wind-turbine
- IEA-22-reference-turbine
- tabular-dataset
- benchmark-dataset
configs:
- config_name: ref
  data_files:
  - split: train
    path: ref/train_damage.csv
  - split: test
    path: ref/test_damage.csv
- config_name: opt1
  data_files:
  - split: train
    path: opt1/train_damage.csv
  - split: test
    path: opt1/test_damage.csv
- config_name: opt2
  data_files:
  - split: train
    path: opt2/train_damage.csv
  - split: test
    path: opt2/test_damage.csv
---

# FLOATBench: Wind Turbine Tower Damage

[**Paper**](https://huggingface.co/papers/2605.25717) | [**Project Page**](https://joao97ribeiro.github.io/FLOATBench/) | [**GitHub**](https://github.com/Joao97ribeiro/FLOATBench)

Tabular fatigue dataset for 22 MW floating offshore wind turbine (FOWT) towers. Contains 582,120 labelled tower section fatigue damage records across three tower geometries: the IEA-22 reference turbine baseline (`ref`) and two FLOAT-derived re-designs (`opt1`, `opt2`).

FLOATBench is the first FOWT fatigue benchmark for tabular surrogate modeling, offering an evaluation protocol that generalizes to engineering surrogates defined over physical operating envelopes.

## Sample Usage

### Using the `datasets` library
```python
from datasets import load_dataset

# Load the IEA-22 reference turbine baseline
ds = load_dataset('DeCoDELab/FLOATBench', 'ref')
print(ds)
```

### Using `pandas`
```python
import pandas as pd

# Load from local CSVs (after downloading)
train = pd.read_csv("ref/train_damage.csv")
test  = pd.read_csv("ref/test_damage.csv")

# Evaluate on the worst-case wind+wave extrapolation cell
ex_ex = test[(test.wind_group == "Extrapolate") &
             (test.wave_group == "Extrapolate")]
```

## Layout

```
FLOATBench/
├── ref/                      IEA-22 reference turbine baseline
│   ├── data.csv              194,040 rows × 16 cols (raw, no split/regime labels)
│   ├── train_damage.csv       51,840 rows × 18 cols (with regime labels)
│   ├── test_damage.csv       142,200 rows × 18 cols (with regime labels)
│   └── metadata.json         counts, split summary
├── opt1/                     FLOAT-derived re-design
│   └── ...                   same files
└── opt2/                     FLOAT-derived re-design
    └── ...                   same files
```

## Schema

Columns appear in the order below. Each `*_id` grid index sits immediately before the value it indexes (`wind_speed_id` before `wind_speed`, `wave_hs_id` before `wave_hs`, `wave_tp_id` before `wave_tp`).

`data.csv` (16 cols):

```
sim_id, wind_speed_id, wind_speed, mean_wind_speed, std_wind_speed,
wave_hs_id, wave_hs, wave_tp_id, wave_tp, wind_seed_id,
section_id, section_height_m, section_radius_m, section_thickness_m,
damage_weight, damage
```

`train_damage.csv` / `test_damage.csv` (18 cols): same order, with `wind_group, wave_group` inserted right before `damage_weight`.

The tables below describe each column grouped by category.

**Identifiers**

| Column          | Type | Meaning                                                                |
|-----------------|------|------------------------------------------------------------------------|
| `sim_id`        | int  | Unique simulation identifier (ties the 30 sections of one run)         |
| `section_id`    | int  | Tower section index ∈ {1,...,30}, 1 (base) to 30 (top)                 |
| `wind_speed_id` | int  | Grid index ∈ {1,...,22}, ordered by `wind_speed` ascending             |
| `wave_hs_id`    | int  | Grid index ∈ {1,...,7} within each `wind_speed`                        |
| `wave_tp_id`    | int  | Grid index ∈ {1,...,7} within each (`wind_speed`, `wave_hs`)           |
| `wind_seed_id`  | int  | Turbulence seed index ∈ {1,...,6}                                      |

**Environmental features**

| Column            | Type  | Meaning                                            |
|-------------------|-------|----------------------------------------------------|
| `wind_speed`      | float | Nominal hub-height wind speed (m/s)                |
| `mean_wind_speed` | float | Realised 10-min mean hub-height wind speed (m/s)   |
| `std_wind_speed`  | float | Realised 10-min std of hub-height wind speed (m/s) |
| `wave_hs`         | float | Significant wave height (m)                        |
| `wave_tp`         | float | Wave peak period (s)                               |

**Tower section geometry**

| Column                | Type  | Meaning                                            |
|-----------------------|-------|----------------------------------------------------|
| `section_height_m`    | float | Tower section midpoint height along tower axis (m) |
| `section_radius_m`    | float | Tower section outer radius (m)                     |
| `section_thickness_m` | float | Tower section wall thickness (m)                   |

**Regime labels** (only in `train_damage.csv` and `test_damage.csv`)

| Column       | Type | Meaning                                                                  |
|--------------|------|--------------------------------------------------------------------------|
| `wind_group` | str  | `In-train` / `Interpolate` / `Extrapolate` (all train rows are `In-train`) |
| `wave_group` | str  | `In-train` / `Interpolate` / `Extrapolate` (all train rows are `In-train`) |

**Damage targets**

| Column          | Type  | Meaning                                                                                |
|-----------------|-------|----------------------------------------------------------------------------------------|
| `damage`        | float | Miner-summed fatigue damage at the section (dimensionless)                             |
| `damage_weight` | float | Probability of occurrence over the 25-year service life                                |

Lifetime damage at a section is recovered as `sum(damage_i * damage_weight_i)` over all conditions.

## Regime-aware split

The recommended train/test partition is **regime-aware**: an alpha-shape over the joint wind/wave operating envelope partitions test points into `In-train` / `Interpolate` / `Extrapolate` regimes on both the wind and wave axes, populating all nine cells of the 3×3 wind×wave regime grid. Per tower:

| Subset | Rows    | Conditions | Description                                  |
|--------|---------|------------|----------------------------------------------|
| Train  | 51,840  | 288        | All `In-train`/`In-train` cell               |
| Test   | 142,200 | 790        | Spans the remaining 8 wind×wave regime cells |

### Reproducing the split from grid IDs

The partition is **fully determined by the integer grid IDs** (`wind_speed_id`, `wave_hs_id`, `wave_tp_id`) shipped on every row. A row is in train iff its three IDs all fall in the train sets:

```python
TRAIN_WS_IDS = {2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14,
                16, 17, 18, 19, 20, 21}          # 18 of 22
TRAIN_HS_IDS = {2, 3, 5, 6}                      # 4 of 7
TRAIN_TP_IDS = {2, 3, 5, 6}                      # 4 of 7

is_train = (df.wind_speed_id.isin(TRAIN_WS_IDS)
            & df.wave_hs_id.isin(TRAIN_HS_IDS)
            & df.wave_tp_id.isin(TRAIN_TP_IDS))
```

## Citation

```bibtex
@misc{ribeiro2026floatbenchdatasetbenchmarkfloating,
      title={FLOATBench: A Dataset and Benchmark for Floating Offshore Wind Turbine Tower Fatigue},
      author={João Alves Ribeiro and Bruno Alves Ribeiro and Francisco Pimenta and Sérgio M. O. Tavares and Faez Ahmed},
      year={2026},
      eprint={2605.25717},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.25717},
}
```

## License

Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).