تقرير
Machine learning for structural design models of continuous beam systems via influence zones
| العنوان: | Machine learning for structural design models of continuous beam systems via influence zones |
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| المؤلفون: | Gallet, Adrien, Liew, Andrew, Hajirasouliha, Iman, Smyl, Danny |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: 30 pages, 16 figures, 8 tables |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/1361-6420/ad3334 |
| URL الوصول: | http://arxiv.org/abs/2403.09454 |
| رقم الأكسشن: | edsarx.2403.09454 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/1361-6420/ad3334 |
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