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 diferentiable 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.
disassembled structures, where the stock of available
material includes individual structural elements (beams) of
The construction sector is responsible for over 35% of specified length and cross section. 3D gridshell structures
the total waste generation in Europe, and for a large frac- are discrete networks of straight bars (the beams)
contion of the overall energy consumption and greenhouse nected by joints at the nodes [
A possible solution to reduce the environmental and target, learning-based method that aligns the gridshell
societal impact of the construction industry is to recycle beams to the stock of available beams, while at the same
or reuse construction materials. Recycling means repro- time improving the statics performance of the whole
cessing waste to generate new products, while reusing structure. Diferently from existing techniques, which
means recirculating existing elements and using them are either based on heuristics for solving assignment
for new constructions. In particular, increasing reuse in problems or on mixed integer optimization, we leverage
the construction sector has the potential to save material on recent advances in 3D deep learning for architectural
and energy, as it avoids sourcing new materials, and to geometry [
One of the main barriers that hinder reuse in con- shell, and the output is a gridshell whose elements’ shape struction is that the design of structures from stocks of has been modified to reuse as many stock elements as posreclaimed elements is totally diferent from traditional de- sible, while preserving the design intent and optimizing sign. Since the designed structure has to conform to the for statics performance. available elements and their characteristics (e.g., length), The pipeline works in two steps (Figure 2). The first one has to rethink the whole design process. step improves the structural compliance of the gridshell,
In this paper, we propose a method for the assisted by means of a neural network featuring a loss that takes design of 3D architectural free-form gridshells from fully- stress into account; the second step assigns stock beams Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga- to gridshell beams, with the assignment problem modeled nized by CINI, May 29-30, 2024, Naples, Italy using soft constraints included in a diferentiable loss. * Corresponding author. For the first time, we extend stock-constrained struc$ andrea.favilli@isti.cnr.it (A. Favilli) tural optimization to 3D gridshells, which are more com(F. 0L0a0c9c-o0n0e0)4;-09030402--07010026-2(A68.6F-a8v56il7li)(;P0. 0C0i0g-n0o00n2i)-;3787-7215 plex structures than those addressed by the state of the 0000-0001-7892-894X (L. Malomo); 0000-0002-6752-6918 (D. Giorgi) art, mostly 2D structures made of trusses. Also, for the © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License first time we propose a learning strategy to combine Attribution 4.0 International (CC BY 4.0). material reuse and environmental impact minimization with the optimization of structural performance, mandatory to ensure stifness, equilibrium, and durability of 3D architectural structures.
We describe a real-world case study, in which the size and capacity of the beam stock are defined from an existing building to dismantle in Tuscany, Italy. We demonstrate how our single-instance learning method enables the reuse of a large fraction of the available material for the design of statics-aware 3D freeform gridshells. Our approach can contribute to switch to a circular economy, and to alleviate the environmental impact of architectural buildings.
In response to the pressing environmental and economic demands, reuse strategies are being prioritized in architecture and in the construction industry. The initiatives encompass a variety of structures and constitutive elements (e.g., trusses, beams, and panels), and employ diferent optimization paradigms to minimize for example material cost and carbon emissions.
In particular, there has been considerable attention
to the design of new structures starting from stocks of
elements from old structures. Brütting et al. [
Unlike the aforementioned studies, which optimize truss structures, our work targets gridshells consisting of triangular beam nets approximating freeform surfaces. While beams experience axial forces and moments, transverse shear and bending, trusses are only subject to axial forces. Moreover, most existing layout optimization methods seek an optimal shape configuration for the recycled stock from scratch. Instead, we improve an existing design shape provided as input.
Finally, while the works mentioned above assign the
available elements through constrained optimization, we
define soft constraints to frame the reuse problem into a
single-instance machine learning task. We leverage on
[
to build new 3D grdshells, which conform to a given design and are also optimal in terms of statics performance.
Given a 3D gridshell as input with the sought shape, we learn a new, improved gridshell, in which the shape of the single elements is optimized to conform to the stock of available ones, while the overall shape is optimized to improve statics performance.
The idea is to perform multi-target shape optimization on gridshells as a single-instance machine learning task.
Classical multi-target methods usually incorporate all optimization targets into a single loss function, expressed as a weighted sum of components. However, assign- strain energy of the shape corrected by Agent2; in turn, ing weights to components can be tricky, especially if Agent2 displaces vertices to ensure the alignment with diferent scales are involved, and if the targets are con- the recycled stock, taking care not to increase the Chamlficting. Therefore, instead of using a heterogeneous loss, fer distance from the shape produced by Agent1. This we involve the interaction of two agents: a geometric ensures smooth convergence and, as a byproduct, the deep learning model (Agent1 in Figure 2) and a vertex ifnal, optimized design is also consistent with the input displacer (Agent2 in Figure 2). Each agent is driven by shape in terms of geometric features. distinct optimization targets (statics for Agent1, reuse of To test our technique, we identified a disposing induselements for Agent2) and corresponding loss functions. trial building located in Pisa, Tuscany, whose roof bays
Given a triangle mesh representing the initial grid shell are made of truss-like structural units adequate to be structure, the learning model (Agent1) operates on the reused as beams for a new gridshell. We examined the mesh geometry to optimize the mean strain energy of the original project of the donor building and extracted a hetstructure. then, the optimizer (Agent2) corrects the new, erogeneous stock of units whose lengths span from 0.75 learned vertex positions to align the beam lengths with to over 6 meters, with 288 elements available for each of the ones available in the stock. We take into account both the 9 available lengths. Figure 3 shows an example 3D beam lengths and stock capacity. Indeed, the corrections gridshell that could be constructed using the stock of eleensure that the number of beams matched to a particular ments from the disposed building. Figure 3(a, left) shows length does not exceed their capacity in the stock. the input design and the optimized output; (a, middle) the
Our process is framed as a single-instance learning color-coded expected structure deformation in meters; task, where both agents iteratively learn from the input and (a, right) the edge strain energy of the structure under gridshell structure. Iterations consist of interleaved steps service load; red (resp. blue) means higher (resp. lower) of the two agents. Each agent has its own loss (light values. It can be seen that the expected deformation of blue boxes in Figure 2), and collaboration between the the optimized structure is significantly lower, implying agents is achieved through mutual agreement: the learn- better statics performance for the building; analogously, ing model (Agent1) adjusts its weights to enhance the the strain energy on edges is significantly reduced. t u p n i deformation (m)
strain energy (kJ)
Figure 3(b, left) reports a bar chart with the assignment of stock elements, and (b, right) the output gridshell with the location of reused elements (color-coded according to their lengths). A large fraction of stock elements has been reused, apart from the shortest and longest ones (note that longer elements can be cut and then reused, yet cutting comes with a cost). Also, the shape and style features of the original design have been fully preserved.
It is important to underline that our method enables the reuse of stock of elements for any input gridshell design: Figure 3 only shows an example, whereas many other designs could have been produced. This is diferent from what is enabled by existing techniques, which only look for compatible designs, given the available stock.
Therefore, our technique leaves to the architect complete design freedom, while taking care of structural performance and environmental impact.
The reuse of structural elements for the design and fabrication of new architectural structures has the potential to greatly reduce the environmental impact of the construction industry. Increasing reuse comes with the challenge of formulating new design paradigms, which take into account the characteristics of the available material without afecting design freedom. At the same time, such design paradigms should take into account statics performance. In this paper, we define a learning-based technique that addresses all three points above for the design of 3D gridshells, as demonstrated by a real-world case study on the reuse of elements from an existing building.