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2022
Report
Closed Access
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.Source: ISTI Technical Report, ISTI-2022-TR/017, 2022
DOI: 10.32079/isti-tr-2022/017
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 introduce a shape optimization strategy based on automatic differentiation of a loss function, which embeds the static equilibrium problem of a girdshell.Source: ISTI Technical Report, ISTI-2022-TR/017, 2022
DOI: 10.32079/isti-tr-2022/017
Metrics:
See at: CNR ExploRA
2022
Report
Closed Access
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.Source: ISTI Technical Report, ISTI-2022-TR/016, 2022
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.Source: ISTI Technical Report, ISTI-2022-TR/016, 2022
DOI: 10.32079/isti-tr-2022/016
Metrics:
See at: CNR ExploRA
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: IWSS 2023 - Italian Workshop on Shell and Spatial Structures, pp. 549–558, Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
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: IWSS 2023 - Italian Workshop on Shell and Spatial Structures, pp. 549–558, Torino, Italy, 26-28/06/2023
DOI: 10.1007/978-3-031-44328-2_57
Metrics:
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| CNR ExploRA
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 292 (2023). doi:10.1016/j.compstruc.2023.107238
DOI: 10.1016/j.compstruc.2023.107238
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 292 (2023). doi:10.1016/j.compstruc.2023.107238
DOI: 10.1016/j.compstruc.2023.107238
Metrics:
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