The Average Best Solution: A Generative Design Tool for Multi-Objective Optimization of Free-Form Diagrid Structures

This research describes the generative modeling method implemented in an open-source program (Grasshopper) as a computational tool for performance evaluation and multi-objective optimization. It explores the initial steps of the design process to find the most fit design, based on goals defined by the designers, from among all possible solutions. In this context, this thesis uses the computational tool to propose a form-finding model for maximizing structural efficiency and constructability of diagrid structures with complex geometries. In architecture and related disciplines, such as structural engineering, the complexity of the both project and the defined goal, that is caused by several design variables and the myriad of relationships between them, play crucial roles in the design process. For the successful handling of such complicated design processes, the consideration of specific goals, requirements, and overall design quality is central. Therefore, this thesis addresses the need for identification and application of computational methods to effectively handle several issues in this design process: the complexity of parametric modeling of diagrid structures, of those computational modeling issues related to analyzing, evaluating, scoring the performance objectives, and of making the decisions needed for the process of multi- objective optimization. To achieve such a goal, this thesis proposes a generative algorithm that includes a parametric model, computational model and a feedback loop. This kind of form-finding method deployed in the generative algorithm draws from existing research on multi-objective optimization. Most importantly, established articles from the Arup team make up the core concepts used in the algorithm-design process. This thesis uses the generative algorithm as an integrally researched computational tool in its formal and operational research. As such, it proposes a conceptual design for a steel diagrid structure with fixed joints of the New National Gallery in Budapest. Such a form-finding method is based not only on structural efficiency, but also on constructability and architectural goals. In the decision-making process, the complicated relationships between considered objectives make it impossible to find the absolute best design solution that has the best performances in all of them. Instead of finding just one result, the generative algorithm eliminates a number of possible solutions based on their performances. The final decision average best solution, which scores high in all objectives but that does not score the highest in all of them, needs to be made by the designer from the limited number of design solutions

Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Form-finding and Buckling Optimization of Grid Shells using genetic Algorithms

We present a strategy for the design of gridshells where form-found structures are optimised for buckling resistance. A genetic algorithm is employed for the initialisation of pre-stress forces required in form-finding using dynamic relaxation. Dynamic relaxation takes this initial pre-stress, a flat grid, as well as self-weight and nodal loads to calculate a static equilibrium. The structure is then analysed for the estimation of the critical buckling load. Different boundary conditions, structural parameters and typology of connections are compared, including a gridshells with triangular and quadrangular patterns. Optimised structures are measured against the trivial solution, which is a structure where dynamic relaxation is initialised with uniform pre-stress. Our results show the proposed strategy can successfully form find gridshells with improved buckling performance

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit

An Architectural Implementation of Topology Optimization Guided Discrete Structures with Customized Geometric Constraints

This thesis explores the use of Topology Optimization (abbreviated to TO) in architectural design by implementing a Bidirectional Evolutionary Structural Optimization(abbreviated to BESO) type TO script as a guide to create a composition of discrete members with complex geometries. TO is an efficient tool for generating an optimal spatial arrangement of structural members along a load path. In the field of computational design, TO has been employed for form-generation of a range of assembled structures that employ discrete units, as well as continuum structures that employ unified and continuous materials. The most advanced current architectural implementations for continuum structures appear in the design of connections, and for discrete structures within space truss designs. Yet, the use of TO in atypical discrete frame structures with complex geometries remain relatively undeveloped in contemporary practice. This thesis contributes a case study where TO is implemented at two key scales: at the component level, geometrically constrained discrete components are assembled using TO, at the macro level, these components are arranged over a TO-designed body. A review of literature from computational design and structural engineering fields, discussing current TO implementations, as well as presenting case studies, is included. The demonstration within the thesis presents a contemporary architectural design process by using existing Karamba BESO code components within a Grasshopper parametric script. Fine-grained components employed within the facade system are combined using TO to produce a cellular lattice architectonic assembly that refers to traditional Korean ornamental pattern found near the site. This demonstration is evaluated structurally and aesthetically. Analyses of comparative structural models with varying configurations are used to demonstrate the structural efficiency of the proposed design. For the aesthetic evaluation, a series of drawings are included to demonstrate what type of spatial qualities the customized lattice structure would look like. The goal of this thesis is to demonstrate architectural and structural qualities resulting from a hybrid exercise where a TO process is applied to geometrically constrained discrete structures. The approach in this thesis provides compromises where structural efficiency and aesthetics are both reasonably achieved, and may lead to novel designs. Future work could be to create a new TO algorithm that can automate this process for increased structural efficiency

Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm

The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses

Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Performance Assessment Strategies:

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis