Single-instance, multi-target learning of 3D architectural gridshells for material reuse and circular economy

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.