This dataset contains 9 trained surrogate models across all four disciplines predicting the performance of UTS bay elements and a demo Python script.

Model Artifacts

Each surrogate is saved as a .joblib file which stores:

{ 'model': <trained sklearn model>, # Trained model 'scaler': <StandardScaler or None>, # Feature scaler 'feature_names': List[str], # 31 feature names 'model_name': str, # e.g., 'Extra Trees' 'target': str, # Target variable 'discipline': str, # Discipline 'metrics': { 'test_r2': float, 'test_rmse': float, 'test_mae': float, } }

Demo Python script

The Python script predict_bays.py demonstrates how to extract the relevant features from the UTS BHoM data schema and use the trained models to predict the performance of each bay in a slab.

To run the CLI script, (cd) to the directory containing predict_bays.py. By default, the trained models should be located in a models subdirectory within this directory. Alternatively, a custom models directory can be specified using the --models-dir option.

These standard scientific Python packages are required:

pip install numpy pandas scikit-learn joblib

The script can be run with multiple options:

python predict_bays.py # default input python predict_bays.py path/to/my_slab.json # custom input python predict_bays.py input.json --format json # JSON output python predict_bays.py input.json --bay 3 # single bay python predict_bays.py input.json --live-load 3.0 # override defaults python predict_bays.py input.json --models-dir ./models # custom models dir