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2022
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Statics-aware 3D gridshells: a differential approach towards shape optimization
Favilli A, Giorgi D, Laccone F, Malomo L, Cignoni P
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we introduce a shape optimization strategy based on automatic differentiation of a loss function, which embeds the static equilibrium problem of a girdshell.DOI: 10.32079/isti-tr-2022/017
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Favilli A, Giorgi D, Laccone F, Malomo L, Cignoni P
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we introduce a shape optimization strategy based on automatic differentiation of a loss function, which embeds the static equilibrium problem of a girdshell.DOI: 10.32079/isti-tr-2022/017
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2022
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Geometric deep learning for statics-aware 3D gridshells
Favilli A, Giorgi D, Laccone F, Malomo L, Cignoni P
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we inculde a solution to solve this mesh vertex displacement learning problem with a target goal of reducing a physically-based loss function, namely the mean strain energy of a gridshell, by means of a graph neural network. We adopt several geometric input features and discuss their effects on the results.DOI: 10.32079/isti-tr-2022/016
Metrics:
Favilli A, Giorgi D, Laccone F, Malomo L, Cignoni P
In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we inculde a solution to solve this mesh vertex displacement learning problem with a target goal of reducing a physically-based loss function, namely the mean strain energy of a gridshell, by means of a graph neural network. We adopt several geometric input features and discuss their effects on the results.DOI: 10.32079/isti-tr-2022/016
Metrics:
2023
Conference article
Open Access
A geometry-preserving shape optimization tool based on deep learning
Favilli A, Laccone F, Cignoni P, Malomo L, Giorgi D
In free-form architecture, computational design tools have made it easy to create geometric models. However, obtaining good structural performance is difficult and requires further steps, such as shape optimization, to enhance system efficiency and material savings. This paper provides a user interface for form-finding and shape optimization of triangular grid shells. Users can minimize structural compliance, while ensuring small changes in their original design. A graph neural network learns to update the nodal coordinates of the grid shell to reduce a loss function based on strain energy. The interface can manage complex shapes and irregular tessellations. A variety of examples prove the effectiveness of the tool.Source: LECTURE NOTES IN CIVIL ENGINEERING, vol. 437, pp. 549-558. Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
Project(s): Future Artificial Intelligence Research
Metrics:
Favilli A, Laccone F, Cignoni P, Malomo L, Giorgi D
In free-form architecture, computational design tools have made it easy to create geometric models. However, obtaining good structural performance is difficult and requires further steps, such as shape optimization, to enhance system efficiency and material savings. This paper provides a user interface for form-finding and shape optimization of triangular grid shells. Users can minimize structural compliance, while ensuring small changes in their original design. A graph neural network learns to update the nodal coordinates of the grid shell to reduce a loss function based on strain energy. The interface can manage complex shapes and irregular tessellations. A variety of examples prove the effectiveness of the tool.Source: LECTURE NOTES IN CIVIL ENGINEERING, vol. 437, pp. 549-558. Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
Project(s): Future Artificial Intelligence Research
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2023
Journal article
Open Access
Geometric deep learning for statics-aware grid shells
Favilli A, Laccone F, Cignoni P, Malomo L, Giorgi D
This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.Source: COMPUTERS & STRUCTURES, vol. 292
DOI: 10.1016/j.compstruc.2023.107238
Project(s): Future Artificial Intelligence Research, SUN
Metrics:
Favilli A, Laccone F, Cignoni P, Malomo L, Giorgi D
This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main ingredients of the architecture are layers that produce deep vertex embeddings from geometric input features, and a differentiable loss implementing structural analysis. We evaluate the method performance on a benchmark of eighteen free-form grid shell structures characterized by various size, geometry, and tessellation. Our results demonstrate that our approach can solve the shape optimization and form finding problem for a diverse range of structures, more effectively and efficiently than existing common tools.Source: COMPUTERS & STRUCTURES, vol. 292
DOI: 10.1016/j.compstruc.2023.107238
Project(s): Future Artificial Intelligence Research, SUN
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CNR IRIS | ISTI Repository | www.sciencedirect.com | CNR IRIS
2024
Conference article
Open Access
Single-instance, multi-target learning of 3D architectural gridshells for material reuse and circular economy
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
We propose a learning-based method for the assisted design of 3D architectural free-form gridshells which reuse elements from dismantled, old buildings. Given a gridshell design as input, the output is a learned gridshell whose shape has been modified to reuse as many stock elements as possible, while preserving the design intent and optimizing for statics performance. The main idea is to perform multi-target shape optimization as a single-instance machine learning task, featuring differentiable losses that account for both structural and stock constraints. Since our approach enables the reuse of existing elements for new designs, it reduces the need for sourcing new materials and for disposing waste. Therefore, it contributes to switch to a circular economy and alleviate the environmental impact of the construction sector. © 2024 Copyright for this paper by its authors.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 470-475. Naples, Italy, 29-30/05/2024
Project(s): NextGenerationEU programme under the funding schemes PNRR-PE-AI scheme (M4C2, investment 1.3, line on AI) FAIR (Future Artificial Intelligence Research)
Favilli A., Laccone F., Cignoni P., Malomo L., Giorgi D.
We propose a learning-based method for the assisted design of 3D architectural free-form gridshells which reuse elements from dismantled, old buildings. Given a gridshell design as input, the output is a learned gridshell whose shape has been modified to reuse as many stock elements as possible, while preserving the design intent and optimizing for statics performance. The main idea is to perform multi-target shape optimization as a single-instance machine learning task, featuring differentiable losses that account for both structural and stock constraints. Since our approach enables the reuse of existing elements for new designs, it reduces the need for sourcing new materials and for disposing waste. Therefore, it contributes to switch to a circular economy and alleviate the environmental impact of the construction sector. © 2024 Copyright for this paper by its authors.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 470-475. Naples, Italy, 29-30/05/2024
Project(s): NextGenerationEU programme under the funding schemes PNRR-PE-AI scheme (M4C2, investment 1.3, line on AI) FAIR (Future Artificial Intelligence Research)
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