Structuring of Teaching and Learning Situations inArchitectural EducationUsing and Integrating Digital Analysis within Interactive GeneticAlgorithms

Matthias Kulcke1, Wolfgang E. Lorenz2, Gabriel Wurzer31Hamburg University of Technology/HafenCity University Hamburg 2,3TU Wien1matthias.kulcke@tuhh.de 2,3{lorenz|wurzer}@iemar.tuwien.ac.at

BACKGROUNDIn the search for a deeper understanding of aestheticquality in architecture, complexity of a design pro-vides a possible area of comparison. In further detail,varying degrees of complexity may serve as compa-rable characteristics regarding different design pro-posals, either observed in the design as a whole orover different scales (fractal characteristics). Thus,using different methods to determine complexitiescan lead to a deeper insight concerning which roleeach of them plays in the overall aesthetic quality ofa given design. One well established method to de-scribe complexity represented as density and distri-bution of significant lines of a design across differ-ent scales is the fractal analysismethodbox-counting(Lorenz 2012). The authors propose an additionalmethod, called gradient analysis, as part of a greaterpalette to aid design aspect-analysis.

Gradient analysisGradient analysis is an additional method for archi-tects, designers as well as students and scholars toanalyze and subsequently influence the degree ofproportion-complexity of a design (Kulcke et al 2015,2016). This method is based on the assumption thatredundancy reduces the complexity of an objects’appearance. However, the reduction of complexity isnot a quality per se; the matter at hand is for design-ers to achieve a balance between redundancy, whichprovides readability of form, and complexity to sus-

tain interest in it. Moreover, the authors are awarethat, concerning this balance, many different levelsof comparison exist, including, among others, the va-riety of material and colour combinations. However,for themoment the authors decided to focus on pro-portion, expressed by a gradient which is defined bytwo points (diagonal of a rectangle).

The basic idea of redundancy-analysis bygradient-comparisons led to a computer programcalled ‘gradient analysis’, which pairs every signifi-cant point of a design with each of the other points,comparing the gradients of the lines defined by eachpair. By doing so emphasis is put on similar relationsrather than specific relations (e.g. Wagner, 1981).Moreover, relations are not weighted depending ondistance and/or visual highlighting e.g. in form ofmaterial edges, since Gestalt perception does notnecessarily require edges (Kulcke et al, 2015). Theweighting of different perceptual relations will besubject of future studies.

The result of the measurement is a list of pairsof points (coordinates of corners or intersections oflines) and their gradients, given as angles. In addi-tion to the angle redundancy quotient a length re-dundancy quotient is also determined. The angle-redundancy quotient Rα is defined by the numberof different anglesCr divided by the total number ofanglesC (formula 1; Kulcke et al, 2015):

Rα =Cr

C(1)

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Figure 1Scan and retrievingrectanglemeasurements

with r being all repetitions and C being calcu-lated as follows (formula 2; Kulcke et al 2015):

C =

(n∙n− 1

2

)(2)

with n being all single connections. The length re-dundancy quotient is calculated in an analogue fash-ion. In both cases, a tolerance coefficient takes intoaccount that slightly differing angles or lengths maybe perceived as similar and that the drawing of theobjectmay not be accurate. The tolerance coefficientincludes all angles inside a certain range, i.e. anglesthat staywithin that range are counted as a repetitionof the angle they are compared to.

Interactive genetic algorithmIn order to create a responsive system to be usedwithin a design- and/or learning process the gradi-ent analysis has been integrated in an interactive ge-netic algorithm (IGA). Within the IGA it serves to de-termine the fitness value of a given object evaluat-ing the degree of proportion complexity and to cre-ate variations with an optimized degree of propor-tion complexity. In principle, the algorithm consistsof the following steps:

1. Generating the first population of n�parents:Currently two different designs each of whichis defined by significant points.

2. Encoding: The coordinates of all significantpoints are translated into binary code.

3. Crossover: Two encoded parents are cut at adefinite point and their parts are exchanged.

4. Mutation: Digits are swapped at a certain po-sition.

5. Fitness�values: Angle redundancy quotientand length redundancy quotient are calcu-

lated to determine the segment�size onGold-bergs weighted roulette wheel (step 7).

6. User-choice: The user can favour a result forGoldbergs weighted roulette wheel.

7. Generating the next population of n�parents:The next generation depends on the resultofGoldbergsweighted roulettewheel (Coates2010, König 2010) and another user-choice.

Some of the steps above are determined by users’choice and/or including randomization. The processis repeated until a satisfying solution is reached.

IGA WITH GRADIENT ANALYSIS IN THEWORKSHOPIn the workshop participants extensively test the in-teractive genetic algorithm and thus also the inte-grated gradient analysis in a given framework. Sincethe software is undergoing a process of permanentevolution, particular attention is paid to the analysisand discussion of the results. Another focus is laid onthe discussion of possible integrations into learningenvironments.

The presented method of analysis to evaluate acertain layer of design quality is used exemplary totest the integration of design analysis via an IGA intoa design learning context. Alternative methods forevaluating design complexity, like easy access fractalanalysis (Wurzer and Lorenz 2017), may also be inte-grated into a similar design process. Participants areheld to adapt the process to their analytical focus orexpertise within their own teaching environment.

The processThe design process as considered in the workshopconsists of three phases:

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1. Freehand formdevelopmentby sketchingob-jects into a given rectangular field plus im-age enhancement and measurement extrac-tion (Figure 1).

2. Evaluating the intuitively chosen proportionsby using the gradient analysis IGA (Figure 2)and semi-automatically producing alternativeversions on the basis of proportion analysis(Figure 3).

3. Comparing and choosing the final version,thus optimizing or confirming the initial de-sign gained by sketching.

Figure 2IGA user Interfaceand first populationof n�parents (n=2;showing just one ofthe two parent),displaying fitnessvalues

Theworkshop contentAs preparatory steps the participants are familiarizedwith the general theoretical approach of proportioncomplexity and proportion optimization. Then theyare taught to use scanning and an image enhanc-ing process allowing to feed the intuitively reachedobject into the web application to apply the IGA forproportionoptimization. The actual task is presentedas a design task to develop a prefabricated concretefaçade module with two openings, especially focus-ing on a pleasing design considering the placementand size of the windows or doors. The height andwidth of the desired element are given, participantsare asked to choose the openings boundaries as par-allels to the rectangular boundary of the overall ob-ject. The participants are advised to draw a mini-

mum of six variations and encouraged to optimizea favoured first design by iteration i.e. by sketch-ing it again with alteration of measurement, but pre-serving the general idea of the previous design. Af-ter choosing two favourite designs from the sketch-ing phase, these are scanned (Figure 1) and openedwithin the image enhancing software. The sketchedand digitalized rectangles receive an overlay of dig-ital rectangles as a means to retrieve the measure-ments of the openings. These are chosen by the par-ticipants who visually control the process, especiallyregarding if the overlay rectangles still represents oreven betters their design aim. The measurements ofthe favoured two variants can now be entered intotheuser interfaceof thegradient analysis IGA to serveas parents (Figure 2), as the starting point for the in-teractive genetic algorithm. Again in several cyclesthe proportions of the openingswithin the facade el-ement are optimized, chosen by the numerical gra-dient quotient and visual appearance (Figure 3). Af-ter several cycles the participants receive an outputof the favoured solution of each cycle out of whichagain two favoured designs can be chosen. Theseare thenput to comparison to the topdesign solutionfrom the first phase of sketching. Finally each partic-ipant presents his or her work and voices the choiceshe or she has made on the way toward the final de-sign.

Advanced TaskThe integration of user choices remains an importantfocus as the developer intend the tool to be utilizedas a cognitive and analytic aid during the design pro-cess and not as an automatic design generator. Forthe workshop the software has been developed fur-ther in order to allow users to manipulate elementsof the IGA themselves while producing design varia-tions e.g. implementing their own cross over masks(Coates 2010, König 2010).

To gather and evaluate the feedback (of stu-dents) a qualitative approach is in development. Thestatus quo is using narrative interview technique andcomputer aided qualitative content analysis. It is part

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of the workshop to discuss the optimization of andalternatives to this feedback-process.

Figure 3Two examples offirst cycle and userchoice (bottom) fornext cycle,displaying fitnessvalues

PREVIOUS TEST CASESThe implementation in the learning envi-ronmentThe approach has undergone first testing with stu-dents in the winter semester 2016/17. The students’feedback is taken into account to optimize the pro-cess and the interactive algorithm (Poirson et al 2010)for further implementation in design education. Acrucial point is the carefully adjusted use of differ-ent media (sketching in combination with analyz-ing/choosing within the GUI of the IGA). On the basisof testing the IGA within the learning environmentchanges have been made to the algorithm to opti-mize the design process. The comparability of thetask is significantly changed by seemingly small al-terations, respectively given rules and constraints, re-garding the task.

To gather and evaluate student feedback a qual-itative approach is in development. The status quois using narrative interview technique and computeraided qualitative content analysis. It is part of theworkshop to discuss the optimization of and alterna-tives to this feedback-process.

DISCUSSIONAllowing participants to use edges that are not paral-lel to the boundaries of the rectangle, using rounded

corners or organic freeform is of course possible. Butthis should be done in a way that every participantis aware of that possibility, to ensure comparabilityfor later discussions of the results. A possible varia-tion is also to allow for teamwork and to propose toparticipants tomarry their own favourite design fromsketching toone chosen fromacolleague, thusbuild-ing the initial pair for the IGA.

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