Universal Timber Slab: Disciplinary Surrogate Models

Version 1.0
Zorn, Max Benjamin; Wortmann, Thomas, 2026, "Universal Timber Slab: Disciplinary Surrogate Models", https://doi.org/10.18419/DARUS-5801, DaRUS, V1
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Description

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
Subject Computer and Information Science; Engineering
Keyword Machine Learning
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1 File
UTS_SurrogateModels.zip
ZIP Archive - 9.6 MB
Published Mar 20, 2026
15 Downloads
MD5: bf78c6d0ddd19900a62ceae56eb08e77
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This dataset is made available under the following terms. Please confirm and/or complete the information needed below in order to continue.

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Custom Dataset Terms - the following Custom Dataset Terms have been defined for this dataset.

This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the provider of the materials (“Provider”) governing the use of the materials (“Materials”) to be downloaded.



I. Acceptance of this Agreement


By downloading or otherwise accessing the Materials, Downloader represents acceptance of the terms of this Agreement.



II. Modification of this Agreement


Provider may modify the terms of this Agreement at any time. However, any modifications will only be effective for downloads subsequent to such modification. No modifications will supersede any previous terms that were in effect at the time of the Downloader’s download.



III. Licensing of Repository Contents


The contents of the repository are published under different licenses:


  1. Model Artifacts (including all .joblib files): are published under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
  2. Source Code (including predict_bays.py and related scripts): is licensed under the GNU General Public License v3.0 (GPL-3.0) . 
    
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
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See the GNU General Public License for more details:
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Use of the Materials includes but is not limited to viewing, executing, and analyzing the content included in the Materials; running predictions using the surrogate models; comparing outputs with other methods; verifying research results; and extracting and/or adapting parts of the Materials for use in research, academic, or non-commercial projects.


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Curation Status History
StatusDateAssigner
2026-03-20 10:21:20Quynh Ngo
In Review by FoKUS2026-03-20 10:18:57Dorothea Iglezakis
Waiting for Feedback from Author2026-03-20 10:00:33Dorothea Iglezakis
In Review by FoKUS2026-03-18 17:32:55Dorothea Iglezakis
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