Machine learning for structural design models of continuous beam systems via influence zones

• URL: http://arxiv.org/abs/2403.09454v1
• Date: Thu, 14 Mar 2024 14:53:18 GMT
• Title: Machine learning for structural design models of continuous beam systems via influence zones
• Authors: Adrien Gallet, Andrew Liew, Iman Hajirasouliha, Danny Smyl,
• Abstract summary: This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. 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.
• Score: 3.284878354988896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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