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In this paper we introduce LUCI, a Lightweight Urban Calculation Interchange system, designed to bring the advantages of a calculation and content co-ordination system to small planning and design groups by the means of an open source middle-ware. The middle-ware focuses on problems typical to urban planning and therefore features a geo-data repository as well as a job runtime administration, to coordinate simulation models and its multiple views. The described system architecture is accompanied by two exemplary use cases that have been used to test and further develop our concepts and implementations.
In this paper we introduce LUCI, a Lightweight Urban Calculation Interchange system, designed to bring the advantages of a calculation and content co-ordination system to small planning and design groups by the means of an open source middle-ware. The middle-ware focuses on problems typical to urban planning and therefore features a geo-data repository as well as a job runtime administration, to coordinate simulation models and its multiple views. The described system architecture is accompanied by two exemplary use cases that have been used to test and further develop our concepts and implementations.
In this paper we introduce LUCI, a Lightweight Urban Calculation Interchange system, designed to bring the advantages of calculation and content co-ordination system to small planning and design groups by the means of an open source middle-ware. The middle-ware focuses on problems typical to urban planning and therefore features a geo-data repository as well as a job runtime administration, to coordinate simulation models and its multiple views. The described system architecture is accompanied by two exemplary use cases, that have been used to test and further develop our concepts and implementations.
Entwerfen Versionieren: Probleme und Lösungsansätze für die Organisation verteilter Entwurfsprozesse
(2011)
Entwerfen ist ein komplexer Vorgang. Soll dieser Vorgang nicht allein, sondern räumlich verteilt mit mehreren Beteiligten gemeinsam stattfinden, so sind digitale Werkzeuge zur Unterstützung dieses Prozesses unumgänglich. Die Verwendung von Werkzeugen für Ent-wurfsprozesse bedeutet jedoch immer auch eine Manipulation des zu unterstützenden Prozesses selbst. Im Falle von Werkzeugen zur Unterstützung der Kollaboration mehrerer Beteiligter stellen die implementierten Koordinationsmechanismen solche prozessbeeinflussenden Faktoren dar. Damit diese Mechanismen, entsprechend der Charakteristika kreativer Prozesse, so flexibel wie möglich gestaltet werden können, liegt die Anforderung auf technischer Ebene darin, ein geeignetes Konzept für eine nachvollziehbare Speicherung (Versionierung) der stattfindenden Entwurfshandlungen zu schaffen. Der vorliegende Artikel beschäftigt sich mit dem Thema der Entwurfsversionierung in computergestützten kollaborativen Arbeitsumgebungen. Vor dem Hintergrund, dass die Versionierung den kreativen Entwurfsprozess möglichst wenig manipulieren soll, werden technische sowie konzeptionelle Probleme der diskutiert und Lösungsansätze für diese vorgestellt.
In the Space Syntax community, the standard tool for computing all kinds of spatial graph network measures is depthmapX (Turner, 2004; Varoudis, 2012). The process of evaluating many design variants of networks is relatively complicated, since they need to be drawn in a separated CAD system, exported and imported in depthmapX via dxf file format. This procedure disables a continuous integration into a design process. Furthermore, the standalone character of depthmapX makes it impossible to use its network centrality calculation for optimization processes. To overcome this limitations, we present in this paper the first steps of experimenting with a Grasshopper component (reference omitted until final version) that can access the functions of depthmapX and integrate them into Grasshopper/Rhino3D. Here the component is implemented in a way that it can be used directly for an evolutionary algorithm (EA) implemented in a Python scripting component in Grasshopper
Based on the description of a conceptual framework for the representation of planning problems on various scales, we introduce an evolutionary design optimization system. This system is exemplified by means of the generation of street networks with locally defined properties for centrality. We show three different scenarios for planning requirements and evaluate the resulting structures with respect to the requirements of our framework. Finally the potentials and challenges of the presented approach are discussed in detail.
Im vorliegenden Beitrag wird ein Framework für ein verteiltes dynamisches Produktmodell (FREAC) vorgestellt, welches der experimentellen Softwareentwicklung dient. Bei der Entwicklung von FREAC wurde versucht, folgende Eigenschaften umzusetzen, die bei herkömmlichen Systemen weitgehend fehlen: Erstens eine hohe Flexibilität, also eine möglichst hohe Anpassbarkeit für unterschiedliche Fachdisziplinen; Zweitens die Möglichkeit, verschiedene Tools nahtlos miteinander zu verknüpfen; Drittens die verteilte Modellbearbeitung in Echtzeit; Viertens das Abspeichern des gesamten Modell-Bearbeitungsprozesses; Fünftens eine dynamische Erweiterbarkeit sowohl für Softwareentwickler, als auch für die Nutzer der Tools. Die Bezeichnung FREAC umfasst sowohl das Framework zur Entwicklung und Pflege eines Produktmodells (FREAC-Development) als auch die entwickelten Tools selbst (FREAC-Tools).
When working on urban planning projects there are usually multiple aspects to consider. Often these aspects are contradictory and it is not possible to choose one over the other; instead, they each need to be fulfilled as well as possible. Planners typically draw on past experience when subjectively prioritising which aspects to consider with which degree of importance for their planning concepts. This practice, although understandable, places power and authority in the hands of people who have varying degrees of expertise, which means that the best possible solution is not always found, because it is either not sought or the problem is regarded as being too complex for human capabilities. To improve this situation, the project presented here shows the potential of multi-criteria optimisation algorithms using the example of a new housing layout for an urban block. In addition it is shown, how Self-Organizing-Maps can be used to visualise multi-dimensional solution spaces in an easy analysable and comprehensible form.
Die im vorliegenden Buch dokumentierten Untersuchungen befassen sich mit der Entwicklung von Methoden zur algorithmischen Lösung von Layoutaufgaben im architektonischen Kontext. Layout bezeichnet hier die gestalterisch und funktional sinnvolle Anordnung räumlicher Elemente, z.B. von Parzellen, Gebäuden, Räumen auf bestimmten Maßstabsebenen. Die vorliegenden Untersuchungen sind im Rahmen eines von der Deutschen Forschungsgemeinschaft geförderten Forschungsprojekts entstanden.
Das vorliegende Arbeitspapier beschäftigt sich mit der Thematik der Nutzerinteraktion bei computerbasierten generativen Systemen. Zunächst wird erläutert, warum es notwendig ist, den Nutzer eines solchen Systems in den Generierungsprozess zu involvieren. Darauf aufbauend werden Anforderungen an ein interaktives generatives System formuliert. Anhand eines Systems zur Generierung von Layouts werden Methoden diskutiert, um diesen Anforderungen gerecht zu werden. Es wird gezeigt, dass sich insbesondere evolutionäre Algorithmen für ein interaktives entwurfsunterstützendes System eignen. Es wird kurz beschrieben, wie sich Layoutprobleme durch eine evolutionäre Strategie lösen lassen. Abschließend werden Fragen bezüglich der grafischen Darstellung von Layoutlösungen und der Interaktion mit dem Dargestellten diskutiert.
It's not uncommon that analysis and simulation methods are used mainly to evaluate finished designs and to proof their quality. Whereas the potential of such methods is to lead or control a design process from the beginning on. Therefore, we introduce a design method that move away from a “what-if” forecasting philosophy and increase the focus on backcasting approaches. We use the power of computation by combining sophisticated methods to generate design with analysis methods to close the gap between analysis and synthesis of designs. For the development of a future-oriented computational design support we need to be aware of the human designer’s role. A productive combination of the excellence of human cognition with the power of modern computing technology is needed. We call this approach “cognitive design computing”. The computational part aim to mimic the way a designer’s brain works by combining state-of-the-art optimization and machine learning approaches with available simulation methods. The cognition part respects the complex nature of design problems by the provision of models for human-computation interaction. This means that a design problem is distributed between computer and designer. In the context of the conference slogan “back to command”, we ask how we may imagine the command over a cognitive design computing system. We expect that designers will need to let go control of some parts of the design process to machines, but in exchange they will get a new powerful command on complex computing processes. This means that designers have to explore the potentials of their role as commanders of partially automated design processes. In this contribution we describe an approach for the development of a future cognitive design computing system with the focus on urban design issues. The aim of this system is to enable an urban planner to treat a planning problem as a backcasting problem by defining what performance a design solution should achieve and to automatically query or generate a set of best possible solutions. This kind of computational planning process offers proof that the designer meets the original explicitly defined design requirements. A key way in which digital tools can support designers is by generating design proposals. Evolutionary multi-criteria optimization methods allow us to explore a multi-dimensional design space and provide a basis for the designer to evaluate contradicting requirements: a task urban planners are faced with frequently. We also reflect why designers will give more and more control to machines. Therefore, we investigate first approaches learn how designers use computational design support systems in combination with manual design strategies to deal with urban design problems by employing machine learning methods. By observing how designers work, it is possible to derive more complex artificial solution strategies that can help computers make better suggestions in the future.
The motivation to deal with the topic simulation of the settlement development in a city in the past 120 years has been to acquire general methods for the analysis and simulation of settlement development processes on the one hand and to verify these methods on the example of the real development of the city of Vienna on the other hand. We follow the assumption that the underlying processes of the urban development can be reduced to various pronounced but always the same hidden driving forces. The objective is to validate the simulation model by the real settlement development and to provide a solid base for the simulation of possible development scenarios of the city of Vienna. The basis for the validation are digital cellular processed and statistical analysed data of the development of the technical infrastructure, the public transportation systems and the population density in Vienna between 1888 and 2001. The simulation method is based on the technique of Cellular Automata (CA) that permits the simulation of the interaction between a potential field and the development of individual areas. This modelling technique is well known as “reaction diffusion” or “dialectic breakdown”. The CA serves as representation of the examined space and divides this space into individual cells. Each of these cells can save certain information (population density, infrastructure facility, development quality) and exchange them locally with the neighbouring cells. The used model parameters permit the simulation of different spread patterns und spread speeds of a settlement structure. From the results methodological, structural, spatial and temporal regularities of urban development processes are derived.
The structure and development of cities can be seen and evaluated from different points of view. By replicating the growth or shrinkage of a city using historical maps depicting different time states, we can obtain momentary snapshots of the dynamic mechanisms of the city. An examination of how these snapshots change over the course of time and a comparison of the different static time states reveals the various interdependencies of population density, technical infrastructure and the availability of public transport facilities. Urban infrastructure and facilities are not distributed evenly across the city – rather they are subject to different patterns and speeds of spread over the course of time and follow different spatial and temporal regularities. The reasons and underlying processes that cause the transition from one state to another result from the same recurring but varyingly pronounced hidden forces and their complex interactions. Such forces encompass a variety of economic, social, cultural and ecological conditions whose respective weighting defines the development of a city in general. Urban development is, however, not solely a product of the different spatial distribution of economic, legal or social indicators but also of the distribution of infrastructure. But to what extent is the development of a city affected by the changing provision of infrastructure? As
We present and compare two evolutionary algorithm based methods for rectangular architectural layout generation: dense packing and subdivision algorithms.We analyze the characteristics of the two methods on the basis of three floor plan sce- narios. Our analyses include the speed with which solutions are generated, the reliability with which optimal solutions can be found, and the number of different solutions that can be found overall. In a following step, we discuss the methods with respect to their different user interaction capabilities. In addition, we show that each method has the capability to generate more complex L-shaped layouts. Finally,we conclude that neither of the methods is superior but that each of them is suitable for use in distinct application scenarios because of its different properties.
How does it come to particular structure formations in the cities and which strengths play a role in this process? On which elements can the phenomena be reduced to find the respective combination rules? How do general principles have to be formulated to be able to describe the urban processes so that different structural qualities can be produced? With the aid of mathematic methods, models based on four basic levels are generated in the computer, through which the connections between the elements and the rules of their interaction can be examined. Conclusions on the function of developing processes and the further urban origin can be derived.