PDF | On Jan 7, , Asterios Agkathidis and others published Generative Design. This dissertation argues for one main point: integrating Generative Design as a new stage in the terney.info [25/04/]. Using generative design, Airbus created a new cabin partition for its. A plane. Designed by mimicking natural growth processes, the partition is stronger than.

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Generative Design Pdf

This paper describes a flexible workflow for generative de- sign applied to architectural space planning. We describe this workflow through an application for the. A practical generative design method Key words: generative design, parametric design, evolutionary design, computer aided conceptual design Abstract A. pdf. Generative Design: Form Finding Techniques in Architecture. 10 Pages Generative Published in by Laurence King Publishing Ltd – city road.

To browse Academia. Skip to main content. You're using an out-of-date version of Internet Explorer. Log In Sign Up. A practical generative design method. Sivam Krish. A practical generative design method Key words: It is suitable for complex unquantifiable multi-criteria design problems where designers need to explore design alternatives within vast design spaces.

5 A Generative Design Grammar for a Virtual Gallery

Form Finding Techniques in Architecture Asterios Agkathidis. Form Finding Techniques in Architecture. It seems that questions in architectural education and practice. However, others have by breaking with predictable relationships between promoted adherence to a speciic design method, based form and representation in favour of computationally on rules rather than intuition, and many now argue that generated complexities, thus enabling the development design methods are necessary in order for architects of new topologies.

In their attempt to do so, their attention turned to precedents from nature and science instead. Louis sullivan, Art deco and Art nouveau movements were greatly notre dame one of the most signiicant architects of the modern du Haut chapel inspired by it. Later, ItkE research Pavilion in stuttgart Although Le corbusier tried to systematize his own proportional each of these projects employed diferent tools, they design methods in the book Modulor , praising all mimicked the intelligent processes of living organisms, the golden section as the gateway to beauty.

He also translating them into architecture rather than simply using applied his famous Modulor proportional diagram to them as inspiration for form and appearance. Both are among his most geometrically advanced projects, proving that the Modulor proportion rules could function as a toolbox ofering unpredictable outputs.

Aldo rossi, for example, he built in Moscow, which still stands today igure Finally, Frei otto, investigating tensile membrane structures, developed For oswald Mathias ungers, responding to the site Figure 03 left the Munich olympic stadium in igure 04 — a deinite diagrid on shukhov involved deining common silhouettes and morphological tower in Moscow, highlight of his career otto and rasch Although characteristics, such as materiality, texture, arches, by Vladimir shukhov developed at a time when computers where not used symmetry, roofs and angles, then trying to reproduce Figure 04 right in architecture at all, the design approach adopted Munich olympic these in a new composition.

Mario Botta, on the other hand, uses simple, oten symmetrical, modernist forms in combination with site-inspired materials, colours and traditional building techniques.

His single-family house in Ligornetto, switzerland , for example, bears the characteristic stripes oten found in the region cappellato Design processes driven by performance In contrast to the above, a number of architects and engineers have practised a completely diferent form-inding method. In their attempt to do so, their attention turned to precedents from nature and science instead.

Louis sullivan, Art deco and Art nouveau movements were greatly notre dame one of the most signiicant architects of the modern du Haut chapel inspired by it. Later, ItkE research Pavilion in stuttgart Although Le corbusier tried to systematize his own proportional each of these projects employed diferent tools, they design methods in the book Modulor , praising all mimicked the intelligent processes of living organisms, the golden section as the gateway to beauty.

He also translating them into architecture rather than simply using applied his famous Modulor proportional diagram to them as inspiration for form and appearance. Both are among his most geometrically advanced projects, proving that the Modulor proportion rules could function as a toolbox ofering unpredictable outputs.

Aldo rossi, for example, he built in Moscow, which still stands today igure Finally, Frei otto, investigating tensile membrane structures, developed For oswald Mathias ungers, responding to the site Figure 03 left the Munich olympic stadium in igure 04 — a deinite diagrid on shukhov involved deining common silhouettes and morphological tower in Moscow, highlight of his career otto and rasch Although characteristics, such as materiality, texture, arches, by Vladimir shukhov developed at a time when computers where not used symmetry, roofs and angles, then trying to reproduce Figure 04 right in architecture at all, the design approach adopted Munich olympic these in a new composition.

Mario Botta, on the other hand, uses simple, oten symmetrical, modernist forms in combination with site-inspired materials, colours and traditional building techniques. His single-family house in Ligornetto, switzerland , for example, bears the characteristic stripes oten found in the region cappellato Design processes driven by performance In contrast to the above, a number of architects and engineers have practised a completely diferent form-inding method.

Eisenman applied these techniques in relation to rules of order, developing several projects on this basis, such as the Biocentrum in Frankfurt and the nunotani corporation headquarters in tokyo ; igure 05 Eisenman Models generative design as a cyclical process based on a simple of design capable of consistent, continual and dynamic abstracted idea, which is applied to a rule or algorithm transformation are replacing the static norms of igure It then translates into a source code, which conventional processes.

It is an or conical forms. Grids, between the designer and the design system. A design schema 2.

A means of creating variations 3. The overall scheme of a generative system operating on procedures is given by Bohnack et al. A modification has been made to his diagram Fig. It is important to note here, the central role of the designer in continuously modifying the generative scheme based on the resultant outcomes; by which the solutions space is navigated in search for viable design solutions.

Generative Design Process 2. The closest amongst them are discussed here. It treats the design process as an interlaced combination of constraint optimization, modeling and optimizing the scheme for searching and evaluating designs.

In short, it is an interactive design development process driven entirely by the designer. The designer interacts specifically with 1 variables, 2 constraints, 3 objective functions and 4 search strategy. It strategically separates the design tasks into quantitative and qualitative tasks. It relies on Genetic Algorithms for generating design solutions.

Caldas[29] demonstrates how building performances can be greatly increased by combining parametric generative schemes and building simulation software to evaluate thermal performance. He uses fitness functions and Parento Genetic Algorithms to optimize the chosen multi-criteria design problem.

The generative scheme seems to interface directly with CAD systems to create the variations required for thermal and lighting analysis. His intention is to thoroughly explore the design space and make it apparent to the designer. It is also a designer driven process and does not rely totally on quantitative criteria. It is suitable for form finding problems in structural design where the design space is searched through the use of two layers. An outer layer representing the topology connection patterns and the inner layer representing the geometry of the structure.

Both layers are algorithmically searched. IGDT is designed to dynamically adapt to evolving design criteria and to assist designers in the early conceptual design phase.

It evolves shapes from random blobs. A phenotype is first specified based on the design space and the genotype is specified based on the solution space. A suitable evolutionary algorithm is then chosen and the fitness function is defined. Multi-objective genetic algorithms are then used to evolve the solutions. This is a classic application of evolutionary algorithms for form design. The generated results are compared in section 4. However, the sophisticated geometry and constraint modeling capability of modern CAD systems seem to have subsumed [31] t, he original intent of Shape Grammar.

Despite being developed more than 30 years ago, its adaptation by industry is limited due to various reasons. This is a highly structured but interactive process where design generation is carried out by grammatical design transformations.

An L-system based generative grammatical encoding has been developed by Hornby [33] who is able to demonstrate that the generative encoding of the genetic model is able to create significantly fitter solutions than non-generative encoding. He is able to demonstrate this in the design of a table.

A neural network based approach has been proposed [34], to enable the system to learn the preference of the designer in selecting designs. If this method can be implemented across a range of design problems, it will enable to reduce the cognitive load in the selection process.

The search aspect of the proposed method is closer in many ways to what is known as a morphogenetic approach [35], which focuses on the dynamics of growth. The GDM is composed of six key components: Genotype — is composed of a generic parametric CAD model, list of design parameters and their initial value and initial exploration envelope. Phenotype — generated CAD files that may include build history, built-in relationships and built- in equations.

Exploration envelope — a list of minimum and maximum values of the driving parameters specifying the limits of the design space to be explored. Design Table - a data table that stores the driving design parameters, their initial values and the limits and other data that may be required and the generated design values preferable in an accessible spreadsheet format.

Design Generation Macro — a macro or a spreadsheet function that operates on the design table. It generates random variations of the driving parameters within limits set by the initial design envelope. CAD system - is a parametric CAD engine with a transparent and editable build history, preferably with a 3D geometric kernel with capabilities to manage geometric relationships, engineering equations and connect to external design tables.

We now briefly describe how these components are connected Fig. Creating the genetic model 2.

Setting the initial envelope 3. Generating designs 4.

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Filtering phenotypes 5. The driving dimensions are then set with an initial value using the native dimensioning system of the particular CAD package and stored in the design table Table. The maximum and minimum range of these values are then set individually or as a percentage value to limit the search within an exploration envelope. The genotype here represents the design space and the limits of search in a format that is operable in CAD.

The Design Table Macro then generates random values within the exploration envelope.

Generative design hartmut bohnacker pdf

The CAD system then generates new instances of the designs based on these values. The generated designs are referred to as phenotypes. Performance filters are then used to judge the viability of these phenotypes. The phenotypes that pass through these filters are then considered viable designs. The filters draw values that are directly related to the design parameters such as distances, which may be drawn from the table and values such as volume and weight may be drawn from the CAD package.

If the CAD system posseses a geometric kernel that is able to detect build- failure then it may be used as a geometric filter. Proximity filter may be used to filter out designs that are similar to each other to ensure that the generated designs are somewhat dissimilar.

These steps are described in greater detail Section 3. All designs generated may be saved and retrieved for comparison or design refinement depending on the work process preferred by the designer. GDM also allows the designer to explore design possibilities interactively around a generated design.

This is accomplished by setting the generated parametric values of the phenotype as a new genotype and by re-setting the initial envelope to cover a smaller region of interest Fig 3. Representation of solution space. The performance envelopes represent the parametric limits of the design that satisfy specific requirement.

The viable design space is then the intersection of various performance envelopes. In generative design, designers are faced with the problem of selecting amongst thousands of designs.

This places a significant cognitive burden on the designer. The designer is able to assess only a limited number of design solutions without cognitive fatigue [16].

Hence, the designs presented to the designer for assessment have to be limited in number and widely dispersed with the viable design space. Each instance may be taken to represent a region of design possibilities as shown Fig. Such an approach makes it possible for human designers to explore large regions of design space based on a limited number of design instances.

GDM allows for the continuous evolution of the solution space. Since the parametric representation of design makes the design space easily navigatable, accuracy of the envelopes representing the performance limits becomes less of an issue in the early stages of exploration, where the focus is mainly on the identification of viable regions.

Once the final design is chosen, the region around it can be examined in finer detail and the limiting performance envelopes can be defined with greater certainty 3.

The proposed arrangement affords great flexibility. It allows the designer to change the CAD model, the genotype values and the filters at any stage of the design process. It also allows the designer to apply GDM methods to selected features of the design. It is this feature that enables it to support conceptual designs where the genotype and performance criteria are still under evolution. Performance r ……. Filters s Phenotypes Fig. Generative Design Method — the overall scheme. The genetic model needs to engender a rich set of design possibilities as it represents the design space.

The genetic model should not only capture the common generic geometry of the desired designs but also the common underlying patterns behind the geometry. Nature provides many great examples as to how geometric variations can be created while maintaining an underlying structure. While there are about , beetles that appear to be very different from each other, they all share a pattern of relationships that is common and constant. A well structured genetic model will be able to represent a much wider range of design variations than a poorly structured model.

The genetic model needs to be robust and hold its geometric logic while being subjected to significant and unpredictable random variations during the generative stages of the design.

An example of a family of designs generated [39] out of a single genetic model is shown Fig. The underlyingbase geometry needs to be structured early on or higher up in the design tree and all the less important features at the latter stages or lower down in the design tree. Since most CAD systems construct the geometry sequentially, such an arrangement would prevent non- critical failures of the less important aspects of the design, invalidating the entire design.

Developing the genetic model is a design exercise in itself and it would require quite a few iterations. Janssen identifies some of its key attributes. The genetic model is also an embodiment of knowledge of the design problem and solutions to it. Structuring genotypes with performance objectives is a way of embedding design knowledge into the genotype itself.

The genetic model may contain some embedded requirements. By embedding such requirements into the genetic model, we can be assured that these requirements are met in all generated solutions. But if there are too many of such built-in requirements, we face a real danger of creating an over constrained generative model and a reduced search space. In setting up the initial parameter values, the commonest form of the design needs to be considered.

To achieve this, the start state of a genetic model genotype should be somewhat in the center of the design space representing the most common design. This can be achieved in cases where the commonest form is known; e.

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In other words the designer should avoid extreme representations when creating the initial instance of the genotype.

These limits need to be set with approximate values as their main purpose is to prevent the waste of computation energy in exploring unviable regions of the design space.

Detailed constraint envelopes will further limit the exploration space but in regions that it does not, the initial envelope can be set to act as the default constraint envelope. The initial envelope can also be set as a n- dimensional envelope where n is the number of parameters.

But for now, it is set as an independent, single dimensional, minimum and maximum value. The same can be accomplished by embedded Macros, which we refer to as the Design Generation Macro. Generated designs that are too close to previously generated designs can be discarded using the proximity filter Fig.

A better approach would be to assess the generated designs, to ensure that they are spaced with sufficient distance from each other in the performance space. A measure of geometric differences could, for example be used to ensure visual differentiation of generated designs. But such strategies will require the generation of a large number of designs before they can be assessed, making it computationally costly.

Initially, it is sufficient to set the constraint envelope with reasonable accuracy, primarily as a bounding region to set the limits of design exploration. Once the candidate solutions are identified, the constraints in these regions can be reset with much greater accuracy. GDM allows for the adding, deleting and modification of constrains in the form of filters throughout the design development.

Most parametric CAD packages have built-in engineering functions and connect seamlessly to a slew of analytical packages that may now be used, to assess various performance aspects of the design. The comprehensive mapping of the constraint envelopes for the initial design space may be computationally costly, but it will speed up the evaluation process.

Though it is possible to pre-compile these envelops, it is not recommended; mainly because the genetic model undergoes significant development during the design process. The order of assessment may be determined by computational cost and work process issues that are discussed Section 3.

Once the high potential designs are identified, the regions around them can be explored in much finer detail. In exploring design space, the designer faces the same set of problems faced by a geographer in mapping new and uncharted territory, looking for minerals that are found in only regions with particular combination of geological characteristics.

Many parts of the unmapped territory would represent continuous stretches of unremarkable designs, lacking in value or novelty. There may be regions where interesting things begin to happen.

The exploration of performance space requires a similar approach. The designer will develop a mental map of the design solutions space and be able to move from one design to another Fig. Exploring the design space Performance may become erratic in certain regions. These could well be high potential regions as performances rise or drop dramatically.

Then, beyond a point the designs would hit the edges of the constraint envelopes. These regions are also interesting, as they could be regions where performance could not be further increased due to the limitations imposed by constraints. By negotiating these constraints, the designer may seek to achieve novelty or increase its performance. If the designs are to be evaluated visually, they may be rendered realistically or be rendered real time in 3D. Once the designs are chosen, they can be fine tuned easily due to their parametric nature.

Variations in texture and color may also be explored at this stage. Current CAD systems are mostly history based. Most modern CAD packages with geometric kernels have abilities to set up geometric relationships within the part file. They also have equation editors that allow designers to create equation driven relationship between dimensions. These two features can be used with great advantage in GDM. Though many CAD packages have means of storing data internally, an external data storage approach was preferred for reasons of transparency and for easy connectivity to other analytical packages.

The design table is essentially a spreadsheet with basic spread sheet capabilities which include functionalities to generate random numbers and abilities to work with internal or external macros. Alternatively, a data file can be used with a program that can achieve the same results. Three ways of implementing GDM is discussed here.

The designs are then generated using a simple function that creates a random value between two numbers. The table is may be structured as shown on Table. The generated values in this design table are then read by the CAD program to create the generated instances of the design.

Additional filters can be implemented within the XL table using simple XL functions to filter out delete the designs that fail the set criteria. The advantage of this approach is that the same macro can be used for various generative design projects. They can also write the data in formats required by other analytical packages. Filters too can be structured as macros. One advantage of this approach is the separation of the Generative scheme from the CAD package.

This will enable the building of common data structures that can be shared across CAD platforms.

This approach has significant advantages in terms of ease of use. It greatly reduces the steps involved in setting up the generative scheme and in navigating the design space directly from a CAD environment that the designer is familiar with. It is able to operate directly within the CAD environment Fig.

The external data table is used here purely for storage of chosen designs. An internal data storage is used to save generated data. These maximum and minimum imum va values may be modified later if necessary. A software proximity proximit filter may be applied here to asses if the he generated gener parameters are beyond a threshold d distance distanc of values generated previously to avoidid similar designs being generated. If it is within a certain c Euclidean distance the design is re-gener generated.

This is considered a geometric etric viability via filter that is only possible is CAD systems stems with w geometric kernel capable of detecting ng unviable unviab geometries. If it fails to re-generate the desigdesign, it generates a fresh instance of the he design Go to step 3. This his album albu may then be recalled to narrow down theth selection by comparing the generated designs against each other. This process is executed entirely ntirely fro from within the CAD environment. At anytime time during dur this process, the designer may alter the design, ign, add or delete new design parameters, modify fy the exploration ex envelope or the threshold values that control ontrol the diversity between the generated designs.

Such an arrangement not only provides complete flexibility xibility but b it is almost identical to the normal CAD based design environment used by designers. Some off the unique un implemental aspects of this implementation entation is covered by US Patent 7,, [44].

Selection by comparison 3. In such cases the computational costs becomes an important consideration. The computational time involved for generating designs and filtering through various filters can be estimated as thus. If m is the average model generation time and fn is the percentage of designs passing through filter n and ftn is the time it takes for the filter to evaluate the solutions.

If g is the number of genotypes generated then, the computational time Tn at the nth filter can be evaluated as: The model rebuilding time depends very much in the use of the geometric kernel.

In GDM the designer has the choice of setting requirements as built in equations that can be embedded into the generative scheme, or as filters or as part of the evaluation criteria.

From a computation point of view, the equation would be the more efficient as it would not waste computational resources required in creating and testing a range of unviable solutions.

In essence, equation filters and evaluation criteria are all used for the same purpose — to prune the solution space; but their computational costs differ. Thus, the efficiency of the generative scheme depends on the strategies used in pruning the search space. Individual filters are used to remove unviable designs based on singular criteria. They are best used to represent evaluation conditions that are not easily analytically derivable. Evaluation is best used to represent multi criteria selection processes.

Filters too can be computationally costly; hence it is best to eliminate the unviable designs as early as possible.

This also means that less permissive filters should be placed first to reduce the number of designs passed on for further filtering. More examples of GDM generated designs are also shown Fig.

A further example Fig. It also illustrates the filtering process 4. Industrial designers usually make foam models they initially with low levels of detail.

The same process can be followed in GDM. The action of the internal proximity filter may be observed from the geometric geometr diversity of the generated designs. Generated base forms In most CAD systems, the hide func function and history function can be used to control the geometric build. This allows the designers to switch from high detail to low detail models or to any p point in the earlier build history and also to switch itch off selected details in order to focus their attention ttention on a limited aspects of the design.

Generation of Design Details Once a collection of initial base forms are selected, the designer may progressively add details Fig. The base form may be kept constant if needed. The designer may at any point modify the base form and the switches will modify according to the relationship that have been embedded in the model.

If a certain geometric form is to be maintained its exploration envelope value can be set to a singular value to preserve its current geometry in subsequent rounds of generations. GDM allows designers to explore a wide range of designs as shown Fig.

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