This study proposes an automated two-staged digital method to generate a feasible form-active gridshell given any arbitrary surface. It explores the behaviour of an external low-tech system in the forming process, and portraits a landscape of efficient solutions to address the trade-off between shape approximation and building costs. The product consists in a computational tool, written in Python using the Rhino/Grasshopper interface. As inputs of the process, the procedure uses further tools, which are part of a wider research on timber post-formed gridshells, funded and directed by the Italian firm Gridshell.it in collaboration with the University of Naples. The process comprises three main phases:

Collection of the input geometry from a dynamic relaxation procedure; only boundary constraints are applied in this phase. A target spatial lattice is also collected through the GridMaker tool.

Definition of a pool of external local forming forces, modelled as prescribed translational displacements, and applied to selected internal nodes of the lattice; the structural model is subject to an incremental analysis to solve dynamic equilibrium and the global deformation is collected.

The nodal gaps between the target grid and the computed structure are calculated. The trade-off between the number and positions of the required forces, and the sum of the nodal gaps is studied by means of a multi objective genetic algorithm (MOGA). To verify the method, a case study is hereafter presented and final results are discussed.

This paper presents a deterministic method to design acoustic chambers for outdoor performances, and emphasises the benefits of a computational morphogenetic approach to the problem. It represents the newest outcome of a research program, conducted by ReS-Team and sponsored by VPM, on the development of the acoustic chamber ReS. The research work is based on the interaction between two different topics – Computational Morphogenesis and Acoustics - which proceed on independent but parallel paths. As a computational tool for any Python®-based environment, it aims to fill the gap between the architectural and acoustic design. By means of geometrical acoustics theory, descriptive geometry and genetic algorithms, the acoustic performance of a given topology is optimised according to defined acoustical parameters. The awareness of possible variations in the acoustic configuration is also increased. Applications are meant to predict the acoustic behaviour within any semi-reverberant field of an acoustic environment.