<b>CBeamXP</b>: Continuous Beam Cross-Section Predictors dataset|结构设计数据集|机器学习数据集

<b>CBeamXP: </b><b>Continuous Beam Cross-section Predictors dataset</b>The <b>CBeamXP </b>(<b>C</b>ontinuous <b>B</b><b>eam</b> Cross-section (<b>X</b>) <b>P</b>redictors) is a dataset containing 1,000,000 data-points to be used for machine learning research. Each data-point represents an Ultimate Limit State (ULS) compliant beam from a continuous system consisting out of 11 members with utilisation ratios between 0.97 to 1.00. The predictors include span and uniformly distributed loads (UDLs) which can be used to predict the cross-sectional properties of each beam contained within the dataset. This dataset is publicly available on a <b>CC-BY-4.0</b> licence and was used within the <b>Gallet et al. (2024)</b> journal article "Machine learning for structural design models of continuous beam systems via influence zones" (doi.org/10.1088/1361-6420/ad3334). Publications making use of the CBeamXP dataset are requested to cite the aforementioned journal article.In addition to the dataset, a training script, environment YAML file and a collection of saved models developed in the Gallet et al. (2024) study are available. These can be used to quickly generate user defined neural networks, compare performances and verify the results achieved by the Gallet et al. (2024) investigation.There are 5 files in this directory:CBeamXP_dataset.csvGallet_2024_training_script.pyGallet_2024_environment.ymlREADME.txtsaved_models.zipClick "Download all" (button at the top) to download the files and and look at the README.txt file for further details on the dataset and how to use the training script.