BioForms integrates sacrificial formworks, agent-based computational algorithms and biological growth in the generation of biodegradable internal wall panel systems. These wall panel systems are intended to minimize material waste, utilize local botany and generate a symbiosis between the artificially made and the naturally grown. This is achieved by utilizing local waste as a structural compressive core, mycelium as the binder, and recycled pellets as the architectural skin. Leveraging mycelium’s structural, acoustic and thermal properties, this exploration delves into unique methods of incorporating fungi and waste into architectural construction. The motivations for this research stem from the need to address the building industry’s contribution to climate change, by considering the lifecycle of our materials. BioForms aims to retrofit existing buildings by replacing foam insulation and MDF (medium-density fiberboard) wall panels with biodegradable and recyclable 3D-printed skins embedded with a mycelium core. Analysing mycelium’s reaction to BioForms I, the second iteration, BioForms II, evolves in design complexity and materiality. BioForms II explored robotically fabricated wood-based polylactic acid plastic (PLA) composite materials. Within the second iteration of this research stream, mycelia was both embedded within the compressed fabricated skins and on the external surface. Whilst BioForms explored the generation of biodegradable wall panel systems, the broader aims of this research is aimed at infiltrating biological matter into human-occupied spaces, completely omitting the use of synthetic building materials within the construction industry and advancing the architects relationship to nature in the generation of form.

This paper describes novel computational design, simulation and fabrication techniques employed in the production of a large sound-absorbing sculpture called Phoenix, made entirely from mycelium-composite materials (myco-materials). Myco-materials are composites made of lignocellulosic agricultural waste fibers bound by fungal mycelium and are produced at commercial scale as alternatives for plastics, insulation foam, or styrene. Mycelium composite materials have known acoustical properties that can be tuned according to variables such as growing time, substrate type, substrate size and density. The fabrication method for producing the Phoenix sculpture revisits how we build performative and formal complexity in the most economic and sustainable way. The results indicate the potential for grown materials to be used in retrofit projects, allowing rooms to be customized in various acoustical situations, such as music or speech.

This article seeks to demonstrate the nexus between agent-related technology and the protection of the environment in armed conflicts, looking at how agent-based modelling and simulation (ABMS) can be used as a tool to protect the environment in armed conflicts. It further analyzes the precautionary principle and due regard, as relevant rules, and explains the legal benefits of deploying ABMS to protect and preserve the natural environment. The article argues that the deployment of ABMS helps States to better understand the environmental effects of conflicts, reassess their military activities and comply with the relevant applicable rules and norms.

We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.

Metaphors are powerful tools for design, enabling designers to encapsulate sets of properties and relations as short verbal descriptions. This paper aims to clarify how simple spatial configurations may emerge from concise metaphoric descriptions at the conceptual design phase. To this aim, we propose a framework for a metaphor-based design process. As a basis for the framework, we introduce the concept of “complementary visual potential” – a property which ties the spatial configuration of the objects in the composition with their metaphoric roles. The framework is developed by studying the practice of metaphor-based spatial configuration design in Japanese rock gardens. Accordingly, it is implemented and tested in this context by attempting to generate alternative designs for an existing rock composition in the famous garden of Ryōan-ji. This is followed by a discussion of its possible implications and potential for generalization to other areas of design.

To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.

This paper presents a computational design method to analyze and synthesize representation mechanisms in structural design. The role of shape grammar schemas in analyzing parametric and grammatical structural analysis is discussed, and a set of schema to generate novel structural topology is provided.

I present an implementation of shape grammars that is aimed at supporting designers. It has two parts: a grammar editor and a stand-alone interpreter. The editor is the modeling application Rhinoceros3d using Python scripts. The interpreter is general, is three-dimensional, and supports subshape detection. A grammar is a Rhinoceros3d model; thus users can manipulate all its parts directly and immediately. That is, they can modify any shape without selecting or invoking an editor, and they can lay out the parts of the grammar in any way they find meaningful. Using this approach, which I call a whole-grammar approach, users are shielded from most subdomain tasks, like typing text files or specifying transformations. Informal observations suggest that users of this implementation can work effectively.

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

A computational approach for the design of self-organizing systems is proposed that employs a genetic algorithm to efficiently explore the vast space of possible configurations of a given system description. To generate the description of the system, a two-field based model is proposed in which agents are assigned parameterized responses to two “fields,” a task field encompassing environmental features and task objects, and a social field arising from agent interactions. The aggregate effect of these two fields, sensed by agents individually, governs the behavior of each agent, while the system-level behavior emerges from the actions of and interactions among the agents. Task requirements together with performance preferences are used to compose system fitness functions for evolving functional and efficient self-organizing mechanisms. Case studies on the evolutionary synthesis of self-organizing systems are presented and discussed. These case studies focus on achieving system-level behavior with minimal explicit coordination among agents. Agents were able to collectively display flocking, exploration, and foraging through self-organization. The proposed two-field model was able to capture important features of self-organizing systems, and the genetic algorithm was able to generate self-organizing mechanisms by which agents could form task-based structures to fulfill functional requirements.

We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.