Gallet, A. orcid.org/0000-0003-4939-9916, Liew, A., Hajirasouliha, I. orcid.org/0000-0003-2597-8200 et al. (1 more author) (2024) Machine learning for structural design models of continuous beam systems via influence zones. Inverse Problems, 40 (5). 055011. ISSN 0266-5611
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.
Metadata
Item Type: | Article | ||||
---|---|---|---|---|---|
Authors/Creators: |
|
||||
Copyright, Publisher and Additional Information: | © 2024 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | ||||
Keywords: | machine learning; structural design models; neural networks; influence zone; inverse problems | ||||
Dates: |
|
||||
Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) | ||||
Funding Information: |
|
||||
Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 08 Apr 2024 11:05 | ||||
Last Modified: | 08 Apr 2024 11:05 | ||||
Status: | Published | ||||
Publisher: | IOP Publishing | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1088/1361-6420/ad3334 | ||||
Related URLs: |