Community resilience entails the community’s ongoing and developing capacity to account for its vulnerabilities and function amid and recover from disturbance. A holistic and systematic approach of the community on how it uses material and energy resources or how a society educates the members’ over time is required to learn from the past and adapt to the present and future opportunities and threads. Community resilience has a long history in the local communities, which is embedded in their culture and history around shared values and local knowledge based on a dedicated and robust collaboration among diverse groups of the community and the various actors from different backgrounds. The innovative partnership between various actors such as stakeholders, research entities, local communities, and third sector parties is required to overcome the complexities of resiliency building. Using local knowledge to understand the local needs better is crucial in developing local, sustainable solutions and building community resilience over time.

With increasing advancements in information and manufacturing technologies, there is an ever‑growing need for innovative integration and application of computational design and robotic fabrication in architecture. Hybrid Intelligence in Architectural Robotic Materialization (HI-ARM) provides methods and frameworks that target this need. HI-ARM introduces methodologies and technologies that incorporate computational, fabrication and material intelligence in integrated design-to-robotic-production workflows. The intelligence is explored at multiple architectural scales (Macro, Meso, Micro) through hybridization of building processes or multi-mode robotic production and multi-materiality. Porosity, Hybridity, and Assembly are introduced as main constituents for materialization frameworks relying on computational design and robotic production. These are tested in a series of original experiments that are presented in this thesis together with four peer-reviewed published papers discussing the process of developing integrated design-to-production methodologies in detail. The contributions show how both architectural materialization processes and building products can be customized in different phases and scales. Moreover, the developed discourse and definitions address the impacts of this research through the lenses of computation and automation in research, education, and practice in the fields of Architecture, Engineering, and Construction.

Buildings consist of subsystems and components which have various functional and performance requirements. This inherent multiplicity demands the design and production of multi-material systems with varying and complementary properties and behaviours. This paper discusses a set of methods of digital design modelling and robotic production of hybridity in various architectural scales. In the case studies, the performance criteria serve as the underlying logic of the design and computation. The projects showcase how programmability and customizability of robotic manufacturing allow for establishing feedback loops from the production to design. Three projects are discussed in detail: a hybrid of flexible cork and rigid polystyrene, a hybrid of structural concrete with an intertwined permanent mould, and a hybrid of soft additively deposited silicone and subtractively produced hard foam. Each project has specific design performance criteria, with which a certain level of geometric complexity and variation is accomplished. Therefore, the research objective is to define and materialize the practical and robotically producible ranges of geometric complexities for each of the proposed methods. Additionally, the customization and development of robotic production setups are discussed. The research concludes that multi-materiality achieved through multimode robotic production methods introduces a higher, on-demand, and performance-driven resolution in building systems.

The Design-to-Robotic-Assembly project presented in this paper showcases an integrative approach for stacking architectural elements with varied sizes in multiple directions. Several processes of parametrization, structural analysis, and robotic assembly are algorithmically integrated into a Design-to-Robotic-Production method. This method is informed by the systematic control of density, dimensionality, and directionality of the elements while taking environmental, functional, and structural requirements into consideration. It is tested by building a one-to-one prototype, which is presented and discussed in the paper with respect to the development and implementation of the computational design workflow coupled with robotic kinematic simulation that is enabling the materialization of a multidirectional and multidimensional assembly system.

This paper presents the integration of an Internet of Things wearable device as a personal interfacing node in an intelligent built-environment framework, which is informed by Design-to-Robotic-Production and -Operation principles developed at Delft University of Technology. The device enables the user to act as an active node in the built-environment's underlying Wireless Sensor and Actuator Network, thereby permitting a more immediate and intuitive relationship between the user and his/her environment, where this latter is integrated with physical / computational adaptive systems and services. Two main resulting advantages are identified and illustrated. On the one hand, the device's sensors provide personal (i.e., body temperature / humidity, physical activity) as well as immediate environmental (i.e., personal-space air-quality) data to the built-environment's embedded / ambulant systems. Moreover, rotaries on the device enable the user to override automatically established illumination and ventilation settings in order to accommodate user-preferences. On the other hand, the built-environment's systems provide notifications and feedback with respect to their status to the device, thereby raising user-awareness of the state of his/her surroundings and corresponding interior environmental conditions. In this manner, the user becomes a context-aware node in a Cyber-Physical System. The present work promotes a considered relationship between the architecture of the built-environment and the Information and Communication Technologies embedded and/or deployed therein in order to develop highly effective alternatives to existing Ambient Intelligence solutions.

Shell structures achieve stability through double curvature, which brings about construction challenges. This paper presents a strategy to design and assemble a panelized shell with a bi-stable mechanism aiming to make the assembly process more efficient. The developed prototype has two states of flat and three-dimensional stable configuration. This reconfiguration is achieved by reconfiguring the flattened surface of a shell into a three-dimensional structure using embedded bi-stable joints. In order to apply this approach on free-form double curved shells, a workflow to translate a shell into its flattened state is developed. Discrete components are connected using bi-stable joints, where each joint has two stable states. Once the joints are mechanically activated, they guide the adjacent components contracting and rotating into the three-dimensional configuration. Initial explorations indicate that an edge of a shell will turn into an isosceles trapezoid in the flattened configuration while a node of a conical mesh will turn into a cyclic quadrilateral in the flattened configuration. The flattening process is demonstrated using a free-form vault, while scaled physical porotypes are 3D printed with PLA and tested. Future studies require exploration into applications with construction materials at larger scales.

Robotic Building implies both physically built robotic environments and robotically supported building processes. Physically built robotic environments consist of reconfigurable, adaptive systems incorporating sensor-actuator mechanisms that enable buildings to interact with their users and surroundings in real-time. These robotic environments require Design-to-Production and -Operation (D2P&O) chains that may be (partially or completely) robotically driven. This chapter describes previous work aiming to integrate D2RP&O processes by linking performance-driven design with robotic production and user-driven building operation.

Considering both architectural and constructional aspects of the built environment, hybridity or multi-materiality is essential to generate functional habitable spaces. Buildings consist of subsystems that each require different and sometimes conflicting material attributes and behaviours. In this context, expanding the solution space for material properties in architectural applications can be achieved through the integration of innovative design computation and production methods. With this focus, the paper presents prototyping processes and frames a discourse on robotic materialisation of architectural hybridity, ranging from micro or material to macro or component scales. The paper discusses three case studies, each with a specific focus on digital modelling, computation and robotic production of hybrid systems. The conclusion outlines how robotic fabrication of architectural multi-materiality redefines, informs and tends methods of design computation and materialisation.

Design to Robotic Production (D2RP) establishes links between digital design and production in order to achieve informed materialization at an architectural scale. D2RP research is being discussed under the computation, automation and materialization themes, by reference to customizable digital design means, robotic fabrication setups and informed materialization strategies implemented by the Robotic Building group at Hyperbody, TU Delft.

This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized design-to-production framework informed by optimization strategies based on point clouds. With respect to D2RO, said implementation builds on a previously developed highly heterogeneous, partially meshed, self-healing, and Machine Learning (ML) enabled Wireless Sensor and Actuator Network (WSAN). In this instance, a computer vision mechanism based on open-source Deep Learning (DL) / Convolutional Neural Networks (CNNs) for object-recognition is added to the inherited ecosystem. This mechanism is integrated into the system’s Fall-Detection and -Intervention System in order to enable decentralized detection of three types of events and to instantiate corresponding interventions. The first type pertains to human-centered activities / accidents, where cellular- and internet-based intervention notifications are generated in response. The second pertains to object-centered events that require the physical intervention of an automated robotic agent. Finally, the third pertains to object-centered events that elicit visual / aural notification cues for human feedback. These features, in conjunction with their enabling architectures, are intended as essential components in the on-going development of highly sophisticated alternatives to existing Ambient Intelligence (AmI) solutions.

Building technologies employed today in 2nd and 3rd world countries are imported, expensive, outdated and unsustainable. Highly developed countries, on the other hand, rapidly advance in developing affordable, numerically controlled and robotically supported material- and energy-efficient methods for building on demand. The research team proposes to close this gap by applying advanced design-to-robotic-production (D2RP) technologies developed at Technical University Delft (TUD) to construction problems in 2nd and 3rd world countries. The provided tool base uses refurbished robotic technology, which is retrofitted with state-ofthe-art open source control software, and by employing local approaches and available materials the dependency on imported materials and processes is drastically reduced. The D2RP unit is coupled with the electricity generating Kite Power (KP) system developed at TUD to create a mobile sustainable autarkic unit that can be deployed everywhere.

This paper presents and discusses the development of a materially informed Design-to-Robotic-Production (D2RP) process for additive manufacturing aiming to achieve performative porosity in architecture at various scales. An extended series of experiments on materiality employing robotic fabrication techniques were implemented in order to finally produce a prototype on one-to-one scale. In this context, design materiality has been approached from both digital and physical perspectives. At digital materiality level, a customized computational design framework has been implemented for form finding of compression only structures combined with a material distribution optimization method. Moreover, the chained connection between the parametric design model and the robotic production setup has enabled a systematic study of specific aspects of physicality that cannot be fully simulated in the digital medium. This established a feedback loop for not only understanding material behaviours and properties but also robotically deposit material in order to create an informed material architecture.