Gradient-based prestress and size optimization for the design of cable domes

Pollini, Nicolò

doi:10.1016/j.ijsolstr.2021.03.015

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The exact choice of architecture in terms of depth D, height H and activation functions a d of the network as indicated in equation ( 12) will require experimentation to achieve acceptable performance with a good bias-variance trade-off [56]. More importantly though, the features used for the input vector x 0 will require careful consideration to create a generalisable inverse operator O † inv as set out in equation (11).…”

Section: Choosing An Appropriate Machine Learning Model Archetypementioning

confidence: 99%

“…Together, these efforts ensure that the dataset on which the neural network is trained on covers sufficient breadth in terms of the input and output space to generalise for a wide variety of continuous beam systems. The dataset generated based on the aforementioned design constraints, the concept of influence zones, and the technique of zero-padding were chosen with the aim to maximise the generalisability of the inverse operator for any system size m, UDLs ω and spans L. This leaves only the utilisation ratios u as the remaining input variable in equation (11). Instead of passing utilisation ratios as explicit inputs to the network, it was decided that the dataset will be generated so that all beams closely correspond to the target utilisation ratio u target .…”

Section: Generating An Appropriate Datasetmentioning

confidence: 99%

“…Inverse problems are predominantly solved iteratively [9], and unsurprisingly so is structural design [10], often with the help of structural optimisation such as size [11,12], shape [13,14], topology [15,16] and layout optimisation [17,18]. Provided that a clear objective function exists, these techniques are the state of the art for solving the structural design inverse problem iteratively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Gallet,

Liew,

Hajirasouliha

et al. 2024

Inverse Problems

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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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Section: Choosing An Appropriate Machine Learning Model Archetypementioning

confidence: 99%

Section: Generating An Appropriate Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Gallet,

Liew,

Hajirasouliha

et al. 2024

Inverse Problems

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Parametric Study of a Cable Dome of Geiger-Type

Tulloch,

Ahmed,

El Damatty

2024

Lecture Notes in Civil Engineering

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Genetic Algorithm as a Tool for the Determination of the Self-Stress States of Tensegrity Domes

2023

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The aim of the paper is to find the appropriate self-stress state of the tensegrity structures. The first approach provides exact solutions but is suitable for simple structures. In the second approach proposed in this research, it is assumed that the forces of the self-stressed state are a set of randomly selected values, which are then optimized by a genetic algorithm. This procedure is intended for more elaborate structures, for which the spectral analysis identifies many self-stress states that need to be superimposed. Two approaches are used, i.e., the spectral analysis of the compatibility matrix and the genetic algorithm. The solution procedures are presented on the example of a simple two-dimensional truss. Next, three different tensegrity domes are considered, i.e., Geiger, Levy and Kiewitt. The significant difference between these domes lies in the cable system. The obtained results are compared with those documented in the literature. It follows from the considerations that the self-stressed states found in the literature are not always accurate (forces do not balance themselves). The presented results confirm the effectiveness of the genetic algorithm for finding self-balanced forces of the existing structures. The method is relatively simple and provides sufficiently accurate results.

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Gradient-based prestress and size optimization for the design of cable domes

Cited by 3 publications

References 36 publications

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

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

Parametric Study of a Cable Dome of Geiger-Type

Genetic Algorithm as a Tool for the Determination of the Self-Stress States of Tensegrity Domes

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