Machine learning for structural design models of continuous beam systems via influence zones
<|reference_start|>Machine learning for structural design models of continuous beam systems via influence zones: This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.<|reference_end|>
@article{gallet2024machine,
    title={Machine learning for structural design models of continuous beam systems
  via influence zones},
    author={Adrien Gallet, Andrew Liew, Iman Hajirasouliha, Danny Smyl},
    journal={arXiv preprint arXiv:2403.09454},
    year={2024},
    doi={10.1088/1361-6420/ad3334},
    archivePrefix={arXiv},
    eprint={2403.09454},
    primaryClass={cs.LG}
}