Automatic generation of diverse equilibrium structures through shape grammars and graphic statics

This article presents a computational design methodology that integrates generative (architectural) and analytical (engineering) procedures into a simultaneous design process. By combining shape grammars and graphic statics, the proposed methodology enables the following: (1) rapid generation of diverse, yet statically equilibrated discrete structures; (2) exploration of various design alternatives without any biases toward pre-existing typologies; (3) customization of the framework for unique formulations of design problems and a wide range of applications; and (4) intuitive, bidirectional interaction between the form and forces of the structure through reciprocal diagrams. Design tests presented in this article illustrate the creative potential of the proposed approach and demonstrate the possibility for unbiased explorations of richer and broader design spaces during early stages of design, with much more trial and less error

A generative design method for cultural heritage applications: design of supporting structures for artefacts

This paper presents a Generative Design Method (GDM) for highly customised Cultural Heritage applications concerning the exhibition and conservation of pottery. As a fundamental requirement, archaeological finds must be preserved in their structural integrity. Additionally, when present, the exposition supports must be aesthetically pleasant meaning that they must be non-invasive in the field of view of the observer. Furthermore, each artefact presents a unique geometry, hence its supporting structure must be designed accordingly. The proposed GDM considers these requirements, adopting a synergy of CAD, CAE, and optimisation tools. It is developed through two phases. The first phase, P1, concerns with the structural integrity of the fragment. In this phase, a Parametric Modelling approach is chosen for its ease of use both in the Finite Element Analysis evaluations of artefacts and in the design and optimisations of feasible supporting structures. The output of the phase P1 is the optimised configuration of the functional elements of the support ('Ci') which are the interface region between the support itself and the fragment of pottery. They represent the input of the second phase, P2, that aims to generate lightweight concepts for the complete supporting structure considering the optimal 'Ci' configuration. During this phase, an aesthetics criterion (related to the minimisation of the support's visibility) is also considered to achieve non-invasive supporting structures. Doing so, the GDM provides informed decisions in the early stages of the design activities with a simulation driven approach oriented to manufacturing. In this way, users are able to focus on design requirements since the concept's variants are generated by means of an optimised configuration of standardised components ('Ci') and obstacle geometries

New directions for Artificial Intelligence (AI) methods in optimum design

Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems

Gaps and requirements for applying automatic architectural design to building renovation

The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Data-driven intelligent computational design for products: Method, techniques, and applications

Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the combination and mapping patterns of these design features for the purposes of design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that limit the development and application of DICD, such as specific dataset building, engineering design related feature engineering, systematic methods and techniques for DICD implementation in the entire product design process, etc. In this regard, a systematic and operable road map for DICD implementation from full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The road map reveals the common mechanisms and calculation principles of existing DICD researches, and thus it can provide systematic guidance for the possible DICD applications that have not been explored