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Optimal Forms

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A Master Thesis On Optimization Of Generative Models by Nelson de Jesus Silverio da Silva

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Optimal Forms

  1. 1. Master Thesis (Dissertação de Mestrado) Master in Design and Product Development Engineering(Mestrado em Engenharia da Concepção e Desenvolvimento de Produto) Optimal Forms Generative Modeling Techniques in Optimization Nelson de Jesus Silvério da Silva Leiria, July 2011
  2. 2. Master Thesis (Dissertação de Mestrado) Master in Design and Product Development Engineering (Mestrado em Engenharia da Concepção e Desenvolvimento de Produto) Optimal Forms Generative Modeling Techniques in Optimization Nelson de Jesus Silvério da Silva Scientific Adviser: Dr. Nuno Alves(Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Leiria, Portugal) Scientific Co-Adviser: Dr. Eva Eggeling (Fraunhofer, Austria) Leiria, July 2011
  3. 3. Report submitted to the Polytechnic Institute of Leiria in partial fulfillment of therequirements for the degree of Master in Design and Product DevelopmentEngineering (Mestrado em Engenharia da Concepção e Desenvolvimento deProduto). ISBN: © Polytechnic Institute of Leiria i
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  5. 5. The JuryPresidentVogals iii
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  7. 7. To My Family v
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  9. 9. Acknowledgments To my Scientific Advisor, Prof. Dr. Nuno Alves (Vice-director of CDRSP Research Centre), for the constant attention, motivation and ideas given throughout the course of the thesis and for helping me in achieving success. Thank you also for your friendship and whole-hearted smile. To my Scientific Co-Adviser, Dr. Eva Eggeling (Business Unit Manager of the Visual Computing Fraunhofer Austria Research GmbH), thank you so much for the support and incentive given to me personally, with your warm friendship and also for the support given professionally that made the realization of this thesis possible. To M.Sc. Torsten Ullrich (Researcher at Fraunhofer, Austria), for priceless knowledge, contribution, helps and of course, he‟s sincere friendship, this was crucial for the development of this innovative work. To Prof. Dr. Paulo Bártolo (Director of CDRSP Research Centre) and Prof. Dr. Helena Bártolo (CDRSP Research Centre) for all the support throughout the course of the thesis and for receiving me at the CDRSP Research Centre. To Prof. Dr. Dieter Fellner (Director of the Fraunhofer Institute for Computer Graphics Research - IGD) for giving me the opportunity to develop this work with the CGV Group and Fraunhofer Austria and for allowing me to have access to all the materials and valuable information produced within the CGV/Fraunhofer group. To M.Sc. Volker Settgast (Researcher at Fraunhofer Austria) for all the great tips about Autodesk Maya and Rendering in general and he‟s invaluable friendship. To Dr. Christina Lemke (Architect with projects in Germany and Spain, urban planner, construction biologist & ecologist) for her support, that kindly allowed me to have access to her published PhD thesis work. To my wife, for her patience, for being always there, supporting and encouraging and for understanding all the time I wasn‟t around. To ALL of you, that made this thesis possible, with your comprehension, motivation, support and encouragement. This master thesis was only possible to achieve, due to the good relationships and close partnership that was settled between CDRSP and Fraunhofer Austria. Thank you all so much… vii
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  11. 11. Keywords Procedural, Optimization, Evolutionary, Algorithm, Simulation, BuildingAbstract The generative modeling paradigm is a shift from static models to flexible models. A generative model describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, radius, orientation, etc.). These values are normally chosen according to aesthetic aspects and style. In this study, the model‟s parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. The field of application is energy and architecture. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building‟s energy consumption (solar energy, heating and insulation). Also, this work aims the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine. In this study, one can read about state of the art developments related to architecture and procedural modeling. The major contribution of this development is to present methods that lead to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains), this will help in the construction of real buildings that account for less energy consumption and for a more sustainable world. ix
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  13. 13. Table ofContents xi
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  15. 15. Acknowledgments ......................................................................................................................................... vii Keywords ........................................................................................................................................................ ix Abstract.......................................................................................................................................................... ixTable of Contents....................................................................................................................................... xiFigures List ...............................................................................................................................................xviiTables List ................................................................................................................................................ xxvAbbreviations and Acronyms .................................................................................................................. xxixI. Introduction ......................................................................................................................................... 1 1 The Problematic .................................................................................................................................. 1 1.1 Thesis Structure ............................................................................................................................... 4II. State of the Art ................................................................................................................................. 7 2 Overview ............................................................................................................................................. 7 2.1 Parametric and Procedural Modeling .............................................................................................. 8 2.1.1 Plugins for existent 3D Software: Blender, 3D Studio Max and Others ....................................... 9 2.1.2 CityEngine .................................................................................................................................. 10 2.1.3 Bentley – MicroStation Extension: GenerativeComponents (GC).............................................. 13 2.1.4 Rhinoceros and Grasshopper ..................................................................................................... 14 2.1.5 Generative Modeling Language (GML) ...................................................................................... 16 2.1.6 Euclides Framework and JavaScript ........................................................................................... 18 2.1.7 Autodesk Revit Architecture 2012 ............................................................................................. 19 2.1.8 Project Vasari and Project Nucleus ............................................................................................ 21 2.1.9 Autodesk Adaptive Components ............................................................................................... 23 2.2 Evolutionary Architecture and the Use of Algorithms in Optimization of Problems ..................... 25 2.2.1 Differential Evolution (DE) ......................................................................................................... 28 2.2.2 Pros and Cons of using Evolutionary Algorithms (EAs) .............................................................. 38 2.2.3 Other Evolutionary Based Algorithms - DE/EDA and Hybrid-DE ................................................ 39 2.3 Advanced Rendering, Visualization and Interaction Techniques in Architecture ......................... 40 2.3.1 Multitouch (MTT) ....................................................................................................................... 40 2.3.2 Virtual and Augmented Reality .................................................................................................. 43 2.3.3 Computer Generated Holography ............................................................................................. 47 2.3.4 Advanced Rendering .................................................................................................................. 50 xiii
  16. 16. 2.3.5 Virtual World Interactivity ......................................................................................................... 53 2.4 “A World Full of Sensors” .............................................................................................................. 57 2.4.1 Sensors Feed Information into Virtual Worlds .......................................................................... 57 2.4.2 Remote Monitoring of Persons Inside Buildings ........................................................................ 60 2.4.3 Kinetic, Responsive Performative and Adaptive Architecture ................................................... 61 2.5 Reverse Engineering and Rapid Prototyping ................................................................................. 62 2.5.1 Reverse Engineering................................................................................................................... 63 2.5.2 Rapid Prototyping ...................................................................................................................... 68 2.5.3 Rapid Prototyping Techniques ................................................................................................... 69 2.5.4 Personal Fabrication and Future Manufacturing ....................................................................... 76 2.6 Building Information Modeling (BIM) and Automated Construction of Buildings ........................ 78 2.6.1 Building Information Modeling (BIM) ........................................................................................ 78 2.6.2 Automated Construction of Buildings ........................................................................................ 79III. Simulation Tools in Architecture ..................................................................................................... 81 3 Outline............................................................................................................................................... 81 3.1 EnergyPlus and DesignBuilder ....................................................................................................... 82 3.2 Autodesk Ecotect Analysis ............................................................................................................. 84 3.2.1 Short Comparison between Autodesk Ecotect and EnergyPlus ................................................. 87 3.3 Ansys: AirFlow ............................................................................................................................... 88 3.4 Sustainability Tools in Architecture – Comparison Studies/Audits ............................................... 90IV. A Global Optimization Framework ................................................................................................. 91 4 Problematic of “Form Follows Energy” and the Pursuit of Solutions................................................ 91 4.1 Answer to the Problematic: Optimal Forms - A Global Optimization Framework ........................ 97 4.2 Identification of Essential Forms Used in the Real World ............................................................. 98 4.2.1 Procedural Shape Generation .................................................................................................... 99 4.2.2 Code Writing Using Euclides and JavaScript ............................................................................ 101 4.3 Simulation Tools Integration ....................................................................................................... 102 4.3.1 Simulation in Ecotect and the Admittance Method................................................................. 104 4.3.2 Initial Manual Workflow Tests ................................................................................................. 105 4.4 Differential Evolution................................................................................................................... 107 4.5 The Developed Global Optimization Framework ........................................................................ 110 4.6 Case Studies and Presentation of Results.................................................................................... 112 4.6.1 Case Study 1 – Classic Shape Building Optimization ................................................................ 113 4.6.2 Case Study 1 - Presentation of Results..................................................................................... 116 4.6.3 Case Study 2 – Cube Shape Building Optimization .................................................................. 126xiv
  17. 17. V. Conclusions and Future Work ....................................................................................................... 131 5 Summary ......................................................................................................................................... 131 5.1 Final Conclusions ......................................................................................................................... 132 5.2 Future Work................................................................................................................................. 133REFERENCES ............................................................................................................................................ 135 xv
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  19. 19. Figures List xvii
  20. 20. xviii
  21. 21. FIG. II-1 – SUICIDATOR CITY GENERATOR (SCG) FOR BLENDER 9FIG. II-2 – PROCEDURAL/PARAMETRIC EXAMPLES CREATED IN CITYENGINE 10FIG. II-3 – CITYENGINE IDE AND THE RULE EDITOR CAPABILITIES 12FIG. II-4 – GENERATIVECOMPONENTS (GC) IDE, BENTLEY MICROSTATION 13FIG. II-5 – RHINOCEROS IS USED IN MULTIPLE FIELDS, INCLUDING ARCHITECTURE 14FIG. II-6 – VORONOI EXAMPLES, CREATED USING RHINOCEROS AND GRASSHOPPER, BY ATSUO NAKAJIMA (TOKYO, JAPAN) 15FIG. II-7 - PARAMETRIC STRATEGIES ACHIEVED USING RHINO AND GRASSHOPPER. (CREATED BY THE AUTHOR OF THIS MASTER THESIS) 15FIG. II-8 - CREATION OF A SIMPLE HOUSE MODEL USING GML, THE EXTRUDE OPERATOR IS REPEATEDLY APPLIED TO THE GROUND POLYGON. TO CREATE THE ROOF, THE COMBINED OPERATOR COLLAPSE-MID IS APPLIED TO THE FACECW AND FACECCW EDGES OF THE EDGE RETURNED BY THE EXTRUDE OPERATION. 16FIG. II-9 – PARAMETERIZATION/CONFIGURATION OF A CHAIR WITH GML 17FIG. II-10 – GOTHIC STYLE BUILDING GENERATED WITH GML 17FIG. II-11 – CONFIGURATION OF DIFFERENT WHEEL RIM STYLES USING GML 17FIG. II-12 – EXAMPLE OF A 3D APPLICATION CREATED FOR THIS THESIS USING EUCLIDES AND JAVASCRIPT. IT ALLOWS THE CONTROL OF SEVERAL SHAPE PARAMETERS ON THE “CLASSIC BUILDING FORM EXAMPLE”. 18FIG. II-13 – ENERGY CONSUMPTION STUDY USING AUTODESK REVIT 19FIG. II-14 – CONCEPTUAL DESIGN IN AUTODESK REVIT ARCHITECTURE 20FIG. II-15 – SUN STUDIES USING PROJECT “VASARI” 21FIG. II-16 - PANEL STUDY USING REVIT AND VASARI 22FIG. II-17 - USING REVIT, VASARI AND NUCLEUS PHYSICS FOR A PANEL STUDY, PLUS ANALYSIS 22FIG. II-18 – ADAPTIVE COMPONENTS IN AUTODESK REVIT 23FIG. II-19 – ADAPTIVE PANEL EXAMPLE IN AUTODESK REVIT 24FIG. II-20 – ANOTHER ADAPTIVE PANEL EXAMPLE, BUILT USING ADAPTIVE COMPONENTS 24FIG. II-21 – IMAGE TAKEN FROM THE BOOK “THE SELFISH GENE” BY RICHARD DAWKINS 25FIG. II-22 – SEVERAL VISIONS RELATED TO EVOLUTIONARY ARCHITECTURE AND BIOMIMETIC 26FIG. II-23 – EVOLUTIONARY EXAMPLES TAKEN FROM THE BOOK “AN EVOLUTIONARY ARCHITECTURE” BY JOHN FRASER [23] 26FIG. II-24 – DYNAMIC GEOMETRY COMPUTATION, “SHANGHAI TOWER - GEOMETRY GENERATE AND RENDERING” (MICHAEL PENG) 27FIG. II-25 – ECOLOGICAL HOUSE OF THE FUTURE (EUGENE TSUI) 27FIG. II-26 – THE GLOBAL OPTIMIZATION PROBLEM (EXAMPLE IN MATLAB). SEARCH OF THE HIGHEST MOUNTAIN PEAK AMONG A NEIGHBORHOOD OF OTHER HIGH MOUNTAINS PEAKS. 28FIG. II-27 - OBSERVATION, ANALYSIS AND COMPUTATION OF BRANCHING PATTERNS IN NATURAL SYSTEMS (BY EVAN GREENBERG [29]) 29FIG. II-28 – SIMPLE EA’S STEPS 32FIG. II-29 – GENERAL EVOLUTIONARY ALGORITHM: I: INITIALIZATION, F(X): EVALUATION, ?: STOPPING CRITERION, SE: SELECTION, CR: CROSS-OVER, MU: MUTATION, RE: REPLACEMENT, X*: OPTIMUM. AUTHOR: JOHANN "NOJHAN" DRÉO 33 xix
  22. 22. FIG. II-30 - DE OPTIMIZATION PERFORMANCE (PEDERSEN, M. [31]) ON SEVERAL DIFFERENT PROBLEMS USING DE/RAND/1/BIN ALGORITHM. PLOTS SHOW THE MEAN FITNESS ACHIEVED. OVER 50 OPTIMIZATION RUNS 34FIG. II-31 – 3D ANAGLYPH AND ACTIVE STEREO VISUALIZATION ON A MULTITOUCH DI TABLE (MTT4ALL MULTITOUCH TABLE WAS BUILT BY THE AUTHOR OF THIS MASTER THESIS) 41FIG. II-32 – INITIAL MTT4ALL SCALE MODEL (LEFT) AND FINAL MTT4ALL FUNCTIONAL PROTOTYPE IN USE , RUNNING FRAUNHOFER VIRTUALDESK APPLICATION, DEVELOPED BY THE AUTHOR OF THIS MASTER THESIS FOR FRAUNHOFER AUSTRIA (RIGHT) 42FIG. II-33 – FRAUNHOFER, MULTITOUCH ARCHITECTURE VISUALIZATION – MESSE FRANKFURT GMBH (USING THE INSTANTREALITY FRAMEWORK) 43FIG. II-34 – ANAGLYPH VISUALIZATION (CREATED BY THE AUTHOR OF THIS MASTER THESIS) 44FIG. II-35 – ACTIVE STEREO VISUALIZATION (CREATED BY THE AUTHOR OF THIS MASTER THESIS) 44FIG. II-36 – AN AUGMENTED REALITY SYSTEM DEVELOPED BY FRAUNHOFER (MONITOR + CAMERA + VIRTUAL REALITY SOFTWARE) 45FIG. II-37 – DAVE, CGV AUSTRIA: IMAGES ARE PROJECTED ON THE BACK PROJECTION SIDE WALLS AND ON THE FLOOR FROM ABOVE, MMIRRORS ARE USED TO REDUCE THE SPACE NEEDED 45FIG. II-38 – CGV AUSTRIA, THE DAVE, A 3D IMMERSIVE SYSTEM (EXPLORING THE NATIONAL LIBRARY OF VIENNA) 46FIG. II-39 – THE SWEETHOME3D (MODELING APPLICATION) OUTPUT IS TRANSFERRED WITH A WEB SERVICE TO AN OPENSG CAVE APPLICATION WHICH LETS THE USER WALK THROUGH A 3D REPRESENTATION OF THE HOUSE PLAN 46FIG. II-40 – HEYEWALL (HIGH RESOLUTION MULTITOUCH SCREEN) 47FIG. II-41 – COMPUTER GENERATED HOLOGRAPHY. A COMPUTER CALCULATES A HOLOGRAPHIC FRINGE PATTERN FOR DISPLAY BY THE SPATIAL LIGHT MODULATOR (SLM), WHICH DIFFRACTS LASER LIGHT TO YIELD AN INTERACTIVE, TRUE 3D IMAGE 48FIG. II-42 – TRADESHOW (AN HOLOGRAM SYSTEM) 49FIG. II-43 – ALTHOUGH HE WAS IN MELBOURNE, TELSTRAS CHIEF TECHNOLOGY OFFICER, HUGH BRADLOW (RIGHT), MAKES IS PRESENCE FELT AT A CONFERENCE IN ADELAIDE (PHOTO: TELSTRA) 49FIG. II-44 – TOUCHABLE HOLOGRAPHY INTERACTION SYSTEM. AN AERIAL IMAGING SYSTEM, A NON-CONTACT TACTILE DISPLAY AND A WIIMOTE-BASED HAND-TRACKING SYSTEM ARE COMBINED. IN THIS FIGURE, THE ULTRASOUND IS RADIATED FROM ABOVE AND THE USER FEELS AS IF A RAIN DROP HITS HIS PALM 50FIG. II-45 – 3D PHOTOREALISTIC RENDERING (CREATED BY HARCHI, AN ARCHITECTURE COMPANY BASED IN PORTUGAL) 51FIG. II-46 – IPL/CDRSP FUTURE BUILDING (RENDERED IN AUTODESK MAYA 2011 BY THE AUTHOR OF THIS MASTER THESIS) 52FIG. II-47 – MEDIUM QUALITY RENDERING OF A FACTORY INSTALLATION (CREATED IN DEEP EXPLORATION BY THE AUTHOR OF THIS THESIS) 52FIG. II-48 – HIGH QUALITY REAL-TIME INTERACTIVE RENDERING (WWW.ICREATE3D.COM) 53FIG. II-49 – MANIPULATION OF VR DATA, PROVIDED BY MEMPHIS [1] (TICIVIEW VR SYSTEM). IGI/FRAUNHOFER RESEARCH GROUP, 2007, SEOUL - SOUTH KOREA, (THE AUTHOR OF THIS MASTER THESIS WAS A MEMBER IN THE TEAM RESPONSIBLE FOR THE DEVELOPMENT OF THIS SYSTEM) 55FIG. II-50 – ADVANCED INTERACTIVE VISUALIZATION (USING IVIEWER), STARTING FROM LEFT TO RIGHT: (A) SKYSCRAPPER; (B) AN APARTMENT INSIDE THE SKYSCRAPPER (WWW.ICREATE3D.COM) 55xx
  23. 23. FIG. II-51 – VIRTUAL FACTORY SIMULATION/SERIOUS GAME THAT WILL ALLOW A COMPANY TO GIVE TRAINING TO USERS, (THIS PROJECT WAS CREATED AT CDRSP BY THE AUTHOR OF THIS MASTER THESIS) 56FIG. II-52 – SCREENSHOT OF THE TRICORDER DEVICE SHOWING THE FLOORPLAN OF A LAB, OVERLAYED WITH PLUG ICONS TO REPRESENT SOUND, LIGHT, CURRENT CONSUMPTION, MOTION AND VIBRATION. ALSO AVERAGE DATE FROM ALL SENSORS IS DISPLAYED [52] 58FIG. II-53 – A PORTAL IN SECOND LIFE SHOWS SENSOR DATA OVER TIME 59FIG. II-54 – A VIRTUAL DATAPOND IN THE VIRTUAL ATRIUM (LEFT) AND A REAL DATAPOND IN THE REAL MEDIA LAB ATRIUM (RIGHT) 60FIG. II-55 – DIFFERENT REPRESENTATIONS OF A PERSON DETECTION IN 3D, STARTING FROM LEFT TO RIGHT: (A) BILLBOARD WITH A THIN COLORED SURROUNDING LINE, (B) ADDITIONAL GEOMETRY, (C, D) OVERLAY MARKER WHICH IS NOT OCCLUDED BY THE SCENE 60FIG. II-56 – A MODEL WHICH IS A KINETIC PAVILION THAT REACTS ON WEATHER DATA 61FIG. II-57 – PHASES OF THE REVERSE ENGINEERING PROCESS 63FIG. II-58 – REVERSE ENGINEERING - CLASSIFICATION TECHNIQUES FOR 3D DATA ACQUISITION 64FIG. II-59 – STEINBICHLER COMET 5 PHOTOGRAMMETRY 3D SCAN EQUIPMENT AT CDRSP REVERSE ENGINEERING LABORATORY 65FIG. II-60 – A 3D SCAN OF A REAL GRAPHITE ELECTRODE USED IN MOLDS INDUSTRY. (3D SCAN AND ANALYSIS/INSPECTION DONE BY THE AUTHOR OF THIS MASTER THESIS, USING STEINBICHLER COMET 5 EQUIPMENT, AND STEINBICHLER COMET PLUS AND COMET INSPECT SOFTWARE) 65FIG. II-61 – “SANTUÁRIO DO SENHOR DA PEDRA”, ÓBIDOS, PORTUGAL 66FIG. II-62 – POINTS OBTAINED FOR THE “SANTUÁRIO DO SENHOR DA PEDRA” BUILDING 66FIG. II-63 – 3D CLOUD OF POINTS FOR THE “SANTUÁRIO DO SENHOR DA PEDRA” BUILDING 66FIG. II-64 - A) STL AND B) 3D MODEL 67FIG. II-65 – SCALE MODEL OF “SANTUÁRIO DO SENHOR DA PEDRA” OBTAINED BY RAPID PROTOTYPING 67FIG. II-66 – MAIN COMPONENTS OF A STEREO-LITHOGRAPHY MACHINE 69FIG. II-67 – PROTOTYPES PRODUCED USING STEREO-LITHOGRAPHY 70FIG. II-68 – SIMPLIFIED FDM PROCESS 71FIG. II-69 – SCALE MODELS OBTAINED USING FDM 72FIG. II-70 – SCHEME OF THE LOM PROCESS 73FIG. II-71 – SCALE MODEL FOR OPORTO MUSIC HOUSE, PRODUCED USING LOM (PORTUGAL) 73FIG. II-72 – SCHEME OF THE SELECTIVE LASER SINTERING (SLS) 74FIG. II-73 - 3D PRINTING PROCESS 75FIG. II-74 – COMPLETE SCALE MODEL OBTAINED USING THE 3D PRINTING PROCESS 75FIG. II-75 – COMBINING SEVERAL TECHNOLOGIES/PROCESSES 76FIG. II-76 - A BRUSH MADE IN A 3D PRINTER, USING TWO DIFFERENT MATERIALS, PRINTED SIMULTANEOUSLY INTO A SINGLE AND NOT ASSEMBLED FUNCTIONAL OBJECT (OBJECT INC.) 77 xxi
  24. 24. FIG. II-77 – BIM VIRTUAL INFORMATION (VIRTUAL SIMULTANEOUS VISUALIZATION OF SIX DIFFERENT PHASES OF AN ONGOING BUILDING PROJECT, CREATED USING GRAPHISOFT ARCHICAD PLATFORM) 79FIG. II-78 – VISION FOR AN AUTOMATED SYSTEM FOR AUTONOMOUS CONSTRUCTION OF BUILDINGS (BEHROKH KHOSHNEVIS) 80FIG. II-79 – A REAL PROTOTYPE FOR AN AUTOMATED SYSTEM THAT WILL ALLOW THE CREATION OF BUILDINGS 80FIG. III-1 – ENERGYPLUS SIMULATION ZONES 82FIG. III-2 – DESIGNBUILDER AND ITS BUILDINGS ENERGY EFFICIENCY RATING 83FIG. III-3 - WORKING IN ENERGYPLUS-MODE INSIDE ECOTECT, WHEN DEFINING OPERATIONAL SCHEDULES 84FIG. III-4 - INTERNAL DAYLIGHT FACTORS SHOWN OVER A STANDARD WORKING PLANE 85FIG. III-5 - OVERLAYING A SUN-PATH ON THE MODEL VIEW 85FIG. III-6 - ANNUAL CUMULATIVE SOLAR RADIATION OVER THE EXTERNAL SURFACES 86FIG. III-7 – COLOURED CONTOURS OF THERMAL CONFORT IN A CONFERENCE ROOM PREDICTED FOR A PARTICULAR VENTILATION SYSTEM DESIGN (ANSYS, INC. PROPRIETARY) 88FIG. III-8 – ANSYS CFD MODELLING OF REGIONAL FLOW PATTERNS NEAR CAPE SHOPPING CENTRE (STEPHAN SCHMITT & THOMAS KINGSLEY; QFINSOFT, SA) 89 3FIG. IV-1 – DIFFERENT POSSIBLE BUILDINGS FORMS, ALL WITH 1000M OF VOLUME, THIS CAN ALLOW THE COMPARISON OF RESULTS OBTAINED WITH DIFFERENT FORMS (PHD THESIS OF CHRISTINA LEMKE [88]) 92FIG. IV-2 – DEFINITION OF AN ELEMENTARY VOLUME, ACCORDING TO DEPECKER, P., ET AL. [91] 93FIG. IV-3 – FLOW FIELD AT A STREET INTERSECTION WITH A TALL BUILDING, ILLUSTRATING EXCHANGES BETWEEN THE STREETS AND ADDITIONAL MIXING PROCESSES DUE TO THE LARGE BUILDING 94FIG. IV-4 – VIEW OF GREENHOUSE SHAPES IN E-W ORIENTATION 96FIG. IV-5 – 3D MODELS THAT REPRESENT REAL WORLD FACTORIES 98FIG. IV-6 - SELECTED BASIC FORMS INSPIRED IN REAL WORLD BUILDING SHAPES, STARTING FROM LEFT TO RIGHT: (A) CUBE, (B) CLASSIC AND (C) CYLINDER) CREATED USING EUCLIDES AND RENDERED USING DEEP EXPLORATION 98FIG. IV-7 – TESTS FOR CREATING DIFFERENT 3D SHAPES (AND CONTROLLING ITS PARAMETERS) USING PARAMETRIC EQUATIONS 99FIG. IV-8 – DEFINITION OF PARAMETRIC EQUATIONS IN MAPPLE 14 100FIG. IV-9 - OPTIMIZED CODE GENERATION PRODUCED BY MAPPLE 14 100FIG. IV-10 – PIECE OF JAVASCRIPT CODE TO GENERATE THE 3D CYLINDER SHAPE (CODE ADAPTED FROM PARAMETRIC EQUATIONS AND MAPPLE 14) 101FIG. IV-11 – RESULTING JAVASCRIPT/EUCLIDES INTERFACE THAT ALLOWS THE CONTROL OF EACH SHAPE PARAMETER 101 3FIG. IV-12 - 3D SHAPE GENERATION IN EUCLIDES (1.000 M OF VOLUME); FOLLOWED BY THERMAL ANALYSIS; AND PRESENTATION OF THE ANALYZED SHAPE. OTHER TYPES OF ANALYSIS CAN BE PERFORMED AS WELL. 105 3FIG. IV-13 – ANOTHER 3D SHAPE GENERATION IN EUCLIDES (MAINTAINING 1.000 M ); FOLLOWED BY THERMAL ANALYSIS; AND PRESENTATION OF THE ANALYZED SHAPE 106FIG. IV-14 – EXAMPLE LIST FOR MATERIALS THAT CAN BE USED INSIDE AUTODESK ECOTECT 109FIG. IV-15 - OVERVIEW OF “OPTIMAL FORMS”, THE DEVELOPED AND PROPOSED GLOBAL OPTIMIZATION FRAMEWORK 110xxii
  25. 25. FIG. IV-16 – THE GLOBAL OPTIMIZATION FRAMEWORK RUNNING AUTONOMOUSLY (SIMULATION IN ECOTECT FOLLOWED BY 3D SHAPE GENERATION IN ORDER TO EVOLVE A POPULATION OF NEW BUILDINGS WITH DIFFERENT PARAMETERS USING THE DIFFERENTIAL EVOLUTION ALGORITHM) 111FIG. IV-17 – THE ALGORITHM CHOOSES DIFFERENT INDIVIDUALS (BUILDINGS) FOR ANALYSIS, NOT STOPPING ON THE LOCAL MINIMA THAT WAS FOUND ALONG THE OPTIMIZATION PROCESS AND GIVING ROOM FOR JUMPING THOSE SAME LOCAL MINIMA 116FIG. IV-18 – IN THIS RUN IT WAS GENERATED A COMPLETELY DIFFERENT INDIVIDUAL (BUILDING SHAPE), BUT THE ADMITTANCE VALUE WAS REALLY HIGH AND OTHER BETTER INDIVIDUALS WERE FOUND 117FIG. IV-19 – A NEW OPTIMIZATION RUN, THIS TIME USING A DIFFERENT VALUE FOR CROSSOVER (0.6) 118FIG. IV-20 – OTHER “TIGHTER” CONSTRAINS WERE CHOSEN, AND THE RESULTS WERE SLIGHTLY WORST THEN THE INITIAL ATTEMPTS (RUNS 1, 2 AND 3) WHERE A WIDER DOMAIN OF SEARCH WAS USED 119FIG. IV-21 – THE OPTIMIZATION RUNS (1, 2 , 3 AND 4) ARE PLOTTED HERE SIMULTANEOUSLY 120FIG. IV-22 – BY ALLOWING THE ALGORITHM TO GENERATE BUILDINGS THAT COULD USE A DIFFERENT ORIENTATION (LESS CONSTRAINED REGARDING THE ORIENTATION OF THE BUILDING), MORE EVALUATIONS WERE NEEDED, BUT A MUCH BETTER RESULT WAS OBTAINED 121FIG. IV-23 – A NEW CONSECUTIVE OPTIMIZATION RUN (USING EXACTLY THE SAME VALUES) IN ORDER TO CHECK IF THE BEHAVIOR WAS CONSISTENT FROM RUN TO RUN 122 O OFIG. IV-24 – BY ALLOWING THE ALGORITHM TO SEARCH FOR SOLUTIONS IN AN ORIENTATION DOMAIN BETWEEN 0 AND 360 AND BECAUSE THE BUILDING DOES NOT HAVE DOORS OR WINDOWS YET, THE ALGORITHM FOUND A GOOD SOLUTION BY ORIENTING THE BUILDING ON A DIFFERENT DIRECTION (WHEN COMPARED TO RUNS 5 AND 5_1) 123FIG. IV-25 – BY PLOTTING ALL THE INDIVIDUALS GENERATED BY THE GLOBAL OPTIMIZATION FRAMEWORK FOR RUNS (5, 5_1 AND 6) IT’S POSSIBLE TO CHECK THE CONSISTENCE OF RESULTS OBTAINED ON THESE MORE COMPLETE OPTIMIZATION RUNS 124FIG. IV-26 – CASE STUDY 1 (FINAL CLASSIC BUILDING SHAPE) – DAILY AND ANNUAL SUN PATHS + SHADOWS AND DAYLIGHT TH LEVELS AT 14:00 PM, FOR THE 8 OF SEPTEMBER (BASED ON SPECIFIC CLIMATE/WEATHER FILES FOR COIMBRA, O 3 O PORTUGAL); WIDTH = 20 M; HEIGHT = 6 M; ROOF ANGLE = 60 ; VOLUME = 1000 M ; ORIENTATION: 122,10 125FIG. IV-27 – WE CAN OBSERVE THAT AT EVALUATION 289 THE OPTIMIZATION FRAMEWORK HAD ALREADY ACHIEVED A VERY GOOD RESULT (COMPARED TO THE FINAL RESULT), BUT BECAUSE THE STOP CRITERION USED WAS, 1000 EVALUATIONS OR DX = 1.0, THE OPTIMIZATION CONTINUED UNTIL DX = 1.0 FOR FOUR CONSECUTIVE TIMES 128FIG. IV-28 - CASE STUDY 2 (FINAL CUBE BUILDING SHAPE) – DAILY AND ANNUAL SUN PATHS + SHADOWS AND TOTAL TH RADIATION LEVELS AT 14:00 PM, FOR THE 8 OF SEPTEMBER (BASED ON SPECIFIC CLIMATE/WEATHER FILES FOR 3 O COIMBRA, PORTUGAL); WIDTH = 10 M; HEIGHT = 6 M; VOLUME = 1000 M ; ORIENTATION: 121,86 129 xxiii
  26. 26. xxiv
  27. 27. Tables List xxv
  28. 28. xxvi
  29. 29. TABLE III-1 - CHARACTERISTICS OF TWO DIFFERENT SIMULATION TOOLS [83] ................................................................. 87TABLE IV-1 – ACTIVITY LEVEL IN AUTODESK ECOTECT ............................................................................................... 108TABLE IV-2 - THIS TABLE SHOWS SEVERAL RUNS USING THE DEVELOPED FRAMEWORK IN ORDER TO OPTIMIZE THE DESIGN OF A “CLASSIC SHAPE FORM” BUILDING USING THE DIFFERENTIAL EVOLUTION (DE) ALGORITHM. IN THIS TABLE IS ALSO POSSIBLE TO SEE THE DIFFERENT CONSTRAINS AND PARAMETERS USED IN THE OPTIMIZATION PROCESS .................................... 114TABLE IV-3 - RESULTS OBTAINED IN EACH OF THE OPTIMIZATION RUNS (ACCORDING TO TABLE IV-2).................................. 115TABLE IV-4 – THIS TABLE SHOWS AN OPTIMIZATION RUN USING THE DEVELOPED FRAMEWORK IN ORDER TO OPTIMIZE THE DESIGN OF A “CUBE SHAPE FORM” BUILDING USING THE DIFFERENTIAL EVOLUTION (DE) ALGORITHM. IN THIS TABLE IS ALSO POSSIBLE TO SEE THE DIFFERENT CONSTRAINS AND PARAMETERS USED IN THE OPTIMIZATION PROCESS ....................... 127TABLE IV-5 - RESULTS OBTAINED IN EACH OF THE OPTIMIZATION RUNS (ACCORDING TO TABLE IV-4).................................. 128 xxvii
  30. 30. xxviii
  31. 31. Abbreviationsand Acronyms xxix
  32. 32. xxx
  33. 33. A DANM: Annealed Nelder and Mead strategy · 37 DAVE: Definitely Affordable Virtual EnvironmentAR: Augmented Reality · 42, 44, 53, 54, 55 (Immersive VR System Developed by Fraunhofer) ·ASA: Adaptive Simulated Annealing · 37 45, 46ASHRAE: American Society of Heating, Refrigerating DDE: Dynamic Data Exchange · 87 and Air Conditioning Engineers · 87 DE: Differential Evolution · 4, 28, 32, 33, 34, 35, 36, 37, 39, 107, 112, 114, 127B EBGA: Breeder Genetic Algorithm · 37BIM: Building Information Modeling · 11, 19, 21, 78, 79 EA: Evolutionary Algorithm · 28, 32, 38BLAST: Building Loads Analysis and System EASY: Evolutionary Algorithm with Soft Genetic Thermodynamics · 82, 83 Operators · 37 EDA: Estimation of Distribution Algorithm · 39 ES: Evolutionary Strategies · 37C ESRI: Environmental Systems Research Institute · 10 ESTG: Superior School of Technology and ManagementCAD: Computer Aided Design · 8, 10, 14, 61, 63, 65, 78, ·2 79, 125, 133 Euclides: Fraunhofer JavaScript Procedural Modeler ·CAM: Computer Aided Manufacturing · 14 2, 4, 5, 18, 81, 97, 98, 100, 101, 102, 103, 105, 106,CAVE: Cave Automatic Virtual Environment · 42, 45, 46 107, 109, 112CC: Contour Crafting (Automated Construction System) · 80CDRSP: Centre for Rapid and Sustainable Development F of the Product · vii, 2, 7, 41, 43, 52, 56, 65, 68CEP: Complex Event Processing · 59 FAR: Floor Area Ratio · 12CFD: Computational Fluid Dynamics · 88, 89, 133 FCT: Portuguese Foundation for Science andCGA: Computer Generated Architecture (shape Technology · 7 grammar) · 11 FDM: Fused Deposition Modeling · 68, 70, 71, 72CGH: Computer Generated Holography · 47, 48, 49CIBSE: Chartered Institution of Building Services G Engineers · 87, 104, 105, 141CNC: Computer Numeric Control · 14 GA: Genetic Algorithm · 30CPU: Central Processing Unit · 51, 52 GC: Bentley Microstation GenerativeComponents · 13CR: Crossover · 34 GFA: Gross Floor Area · 12CSG: Constructive Solid Geometry · 16 GIS: Geographic Information Systems · 10, 40CT: Computed Tomography · 51 GML: Generative Modeling Language · 16, 17, 18 GPU: Graphics Processing Unit · 51, 52 xxxi
  34. 34. H OHDE: Hybrid Differential Evolution · 39 OLED: Organic Light-Emitting Diode · 58HSM: High Speed Machining · 68HVAC: Heating, Ventilation and Air Conditioning · 82, P 83 PDM: Product Data Management · 8I PLM: Product Lifecycle Management · 8ICEO: IEEE Competition on Evolutionary Optimization · R 37IEEE: Institute of Electrical and Electronics Engineers · Rhino: (a.k.a. Rhinoceros), its a commercial NURBS- 37 based 3D modeling tool, developed by RobertIPL: Polytechnic Institute of Leiria · 2, 52 McNeel & Associates · 14; Rhinoceros (Robert McNeel & Associates) · 14, 15L RICS: Royal Institution of Chartered Surveyors · 90, 140LUA: Lightweight multi-paradigm programming S language designed as a scripting language with extensible semantics as a primary goal · 87, 102, SCG: Suicidator City Generator · 9 103, 141 SDE: Stochastic Differential Equations · 37 SLM: Spatial Light Modulator · 48M SLS: Selective Laser Sintering process · 68, 69, 73, 74 STL: A file format native to the stereolithography CADMTT: 2.3.1 Multitouch · 40 software · 67MTT4ALL: Multitouch Table developed by the Author of this master thesis · 41, 42 TN Tabletops: Horizontal Interactive Displays · 40, 41NP: Number of Elements in Each Generation · 34, 36, V 37NURBS: Non-uniform rational basis spline · 14, 16 VEs: Virtual Environments · 53 VR: Virtual Reality · 42, 43, 53, 54, 55 VRML: Virtual Reality Markup Language · 43xxxii
  35. 35. xxxiii
  36. 36. I. Introduction1 The ProblematicThe adjustment of architectural forms to local and specific solar radiation conditions is afundamental study that must be always conducted by architects. When discussing energyconsumption and solar power harness in buildings, important topics of discussion comeinto play, like the real relation between a building form and its energy behavior, or findingthe right building shape for a specific location and weather conditions on an all year basis.Several studies were published so far, to try to answer and demonstrate these and otherimportant questions. Form follows energy, but how exactly is this happening it‟s somehowdifficult to demonstrate without having automated tools and models. One must try tomanually analyze the energy dependence between form and volume. With this kind ofstudies, there is an attempt to simultaneously adapt a building form, in order to increase thepotential areas for solar radiation “reception” and at the same time looks for ways toreduce the thermal loss (here the admittance method, well known by architects, it‟s useful),taking in account the need to design for specific locations and specific weather conditions.This research work aims, to examine the theoretical concepts associated to the problem of“Form Follows Energy”, pointed out, in studies done by some researchers. Also, thepresent study discusses emergent methods based on evolutionary algorithms andenvironmental simulation tools and it targets the development of new design methods thatallow the construction of sustainable optimized buildings by using digital technologies,through the creation of an automated tool and an optimization framework that will allowthe optimization of 3D shapes (buildings), taking in account the geo-location and specificweather conditions, throughout the run of automated simulations, making autonomouschanges and optimizations utilizing evolutionary algorithms. 1
  37. 37. For the creation of this work, a strong collaboration between the Polytechnic Institute ofLeiria/Superior School of Technology and Management/Centre for Rapid and SustainableProduct Development (IPL/ESTG/CDRSP) and Fraunhofer Austria was established.The main research objectives of this work can be listed as follows: (i) To give an overview of state of the art technologies and techniques currently employed in the architecture field, regarding simulation and analysis, visualization, rendering, virtual interaction, rapid prototyping, reverse engineering and automated construction; (ii) To evaluate common shapes used in real world buildings, with focus on greenhouses forms as a practical example case study; (iii) To investigate, in order to obtain the necessary parametric equations (required to the use of computer graphics in the creation of procedural 3D models) for the several identified common building shapes. Also, to extract the essential parameters (height, width, length, orientation, roof angle…) of those fundamental shapes, in order to achieve a fully parametrical defined 3D model, this will allow the use of Euclides (JavaScript Procedural Modeler); (iv) To research on the possibility of having a programming integration with commonly used simulation packages and tools, to simulate how the different shapes of buildings have an influence in energy consumption throughout the life of these real buildings, with the final purpose of developing an automated tool capable of running automated simulations;2
  38. 38. (v) To make use of evolutionary algorithms in order to perform autonomous and automatic optimizations of 3D shapes based on automated simulations and the well-known method of admittance, always employing tools and methods which are widely accepted in the architecture field. The final goal is the creation of a global optimization framework for automatic generation of optimized 3D building forms, also taking in account the specific location weather conditions; (vi) To present and explain the importance of the results achieved with the developed global optimization framework, pointing out new directions in sustainable architecture design;Presently, this study is applied to architecture and sustainability. But it must be referredthat this problematic of evolutionary architecture and simulation tools integration can beextended to other domains/fields, like the industrial or the medical field. They could alsobenefit from an autonomous generation of different 3D shapes, as well as a self-governingoptimization of those same 3D forms. 3
  39. 39. 1.1 Thesis StructureThe thesis is divided into five chapters, which develops in accordance with the identifiedresearch objectives. This first chapter (Introduction) comprises an introduction that inaddition to listing the key objectives, also briefly describes the context of the research.The contents of the remaining chapters are summarized as follows:  State of the Art Reviews the latest work developed around procedural modeling, visualization techniques, digital fabrication and reverse engineering. It also presents and describes a JavaScript Framework, named “Euclides”, utilized for the easy creation of 3D procedural and parametric shapes. This was the procedural framework used in this thesis for the creation of all the necessary parameterized 3D buildings forms. Lastly, a briefly explanation of how evolutionary algorithms work, is also given. Moreover, a specific evolutionary algorithm (Differential Evolution - DE) is described, as well as the reasons why this particularly algorithm was chosen in this thesis, for the development of an automated tool for 3D shapes (buildings forms) optimization. Other evolutionary algorithms are pointed out too, as plausible alternatives to be implemented within the optimization framework in future work, in order to tackle other problematic;  Simulation Tools in Architecture Gives an overview of different interests in simulation, in particular those related to the problematic of architecture and energy consumption in buildings. Some simulation tools/packages are presented, together with the reasons for selecting a particular tool to be used in the work presented here;4
  40. 40.  A Global Optimization Framework Explains the work developed throughout this thesis, on the problematic of how buildings form affects the energy consumption on a daily basis throughout its entire life. Several methodologies used for choosing parameters to control a specific 3D shape as well as other tools used to deduce parametric equations and “mathematical code”, are also described with the objective to show how these 3D parametric models were generated using Euclides and JavaScript. Also, the general concept of the developed optimization framework is explained. A practical overview of the work is given, every framework component is presented in more detail and the achieved results are presented and clarified. Finally a case study is presented, where the problem of automatic optimization is extremely relevant and the results obtained are then presented and explained; Conclusions and Future Work Provides an overall summary of the thesis and points out further progress paths and improvement options for the autonomous global optimization framework that was developed and presented in this thesis; 5
  41. 41. II. State of the Art2 OverviewA review on the state of the art is presented, regarding current work focused on procedural,parametric and adaptive architecture modeling. A short description of evolutionaryalgorithms is given. Also, innovative methods of visualization and presentation ofarchitecture projects are presented, as well as several techniques for rapid prototyping andreverse engineering. These methods and techniques are essential to capture 3D geometry,for achieving more complete results on any architecture project (e.g. production of scalemodels for simulation in wind tunnels, virtual simulation, building control…) and essentialfor presenting the achieved results to final customers (rendering, interactivity…), also, theauthor of this thesis was a research member of the CDRSP Research Centre and earned ascholarship, on the topic “Build-it-Green”, from the Portuguese Foundation for Scienceand Technology (FCT). This “Build-it-Green” topic is closely related to the architecturesubject and some of the work that was developed at CDRSP by the author, was focused onthese same areas and it was conducted throughout the realization of this master thesis.This state of the art review, aims to present a short explanation about each product,methodology or recent development. For getting more insightful details, the correspondentreferences should be further investigated. 7
  42. 42. 2.1 Parametric and Procedural ModelingParametric Computer Aided Design (CAD) modeling assumes, nowadays, an importantrole in the definition of 3D models. There are several active attempts to collect all theinformation about a product or about the different parts that compose a product.Information platforms like Product Data Management (PDM) or Product LifecycleManagement (PLM) [1], offer a way to gather the different distributed data that is vital foran efficient product management. However there is some “intelligent” information thatmust be captured with each part and product assembly, such as parametric information(e.g. width, height, volume, orientation, length, relations between parts, formulas …). Alsosemantic methods, using ontologies, try to present solutions for solving problems like therelationship between different, yet related 3D geometric information [2]. Proceduralmodeling can be viewed as the use of different techniques in computer graphics to create(generate) 3D models and textures from sets of rules. L-Systems, fractals, and generativemodeling are procedural modeling techniques since they apply algorithms for producingscenes. The set of rules may either be embedded into the algorithm, configurable byparameters, or a set of rules that is completely separated from the evaluation engine. Theoutput is then called procedural content, which can be used in computer games, films, beuploaded to the internet while requiring much less bandwidth, or the user may edit thecontent manually [3].Procedural models often exhibit database amplification, meaning that large scenes can begenerated from a much smaller amount of rules. If the employed algorithm produces thesame output every time, the output needs not to be stored. Often, it is sufficient to start thealgorithm with the same random seed to achieve the same result. Although all modelingtechniques on a computer require algorithms to manage and store data at some point,procedural modeling focuses on creating a model from a rule set.Procedural modeling is often applied when it would be too cumbersome to create a 3Dmodel using generic 3D modelers, or when more specialized tools are required, this isoften the case for plants, architecture or landscapes [4].8
  43. 43. 2.1.1 Plugins for existent 3D Software: Blender, 3D Studio Max and OthersThere are many plugins available on the internet for use within commonly used 3Dmodeling software, like Autodesk 3D Studio Max, Autodesk Maya or the open sourcemodeling software Blender and many others that allow the automatic generation of terrain,buildings or even cities in a procedural way.These plugins permit the creation of 3D models according to specified rules and customparameters specified by the user, they can also be customized through the use of scriptinglanguages like, Python or MEL. Suicidator City Generator (SCG) is a wonderful exampleof such plugin for use inside Blender. It is a Python script for Blender or in other words itis a program written in the Python programming language that runs inside the Blenderenvironment [5].It‟s not the purpose of this work to explain in detail how these plugins perform, howeverthey must be mentioned here as an existent and possible path for the creation of generativecomponents in today‟s 3D modeling software packages. Fig. II-1 – Suicidator City Generator (SCG) for Blender 9
  44. 44. 2.1.2 CityEngineCity Engine (now acquired by ESRI) is one of the most successful and powerful examplesfor procedural modeling, it‟s a standalone software that provides a unique conceptualdesign and modeling solution for the efficient creation of 3D cities and buildings, forprofessional users in entertainment, architecture, urban planning, Geographic InformationSystems (GIS) and general 3D content production [4]. CityEngine was also tested in thismaster thesis study. Fig. II-2 – Procedural/parametric examples created in CityEngineThe key highlights of CityEngine include [6]:  GIS/CAD Data Support and OpenStreet Map Import CityEngine supports industry standard formats like, ESRI Shape file or DXF which allow the import/export of any geo-spatial/vector data such as parcels, building footprints with arbitrary attributes, or line data to create street networks. To copy real cities or efficiently create an urban environment for our design, it‟s possible to use data from OpenStreet Map. Geospatial data of real cities can also be downloaded and directly imported it into CityEngine;10
  45. 45.  Dynamic City Layouts and Street Networks Patterns An intuitive toolset is provided to interactively design, edit and modify urban layouts consisting of (curved) streets, blocks and parcels. Street construction or block subdivision is controlled via parametric interfaces, giving immediate visual feedback; CityEngine offers unique street grow tools to quickly design and construct urban layouts. Street patterns such as, grid, organic or circular, are available and the topography of the terrain is taken into account; Rule-based Modeling Core Procedural modeling based on Computer Generated Architecture rules (CGA shape grammar) offers unlimited possibilities to control mass, geometry assets, proportions, or texturing of buildings or streets on a city-wide scale. We can define our own rules using custom textures/models in the node- or text-based rule editor; Facade Wizard and Parametric Modeling Interface One can quickly create rules out of an image or a textured mass model with this simple and easy-to-use visual facade authoring tool. The resulting facade rules are size-independent, contain level-of-detail and can be extended with e.g. detailed window asset. A convenient interface to interactively control specific street or building parameters such as the height or age (defined by the rules) is provided and with the live mode, parameter modifications invoke the automatic regeneration of the 3D model; Map-Controlled City Modeling and Reporting (Building Information Modeling - BIM for Cities) Any parameter of the buildings and streets can be controlled globally via image maps (for example the building heights or the land use-mix); this allows for intuitive city modeling and quick changes on a city-wide scale. Furthermore, terrains can be imported, aligned, and exported. Customized rule-based reports can be generated to analyze the urban design e.g. automatically calculate quantities 11
  46. 46. such as Gross Floor Area (GFA), Floor Area Ratio (FAR), etc. Reports are updated automatically and instantaneously and can be made for whole city parts;  Industry-Standard 3D Formats CityEngine supports Collada, Autodesk FBX, 3DS, Wavefront OBJ and e-on softwares Vue, which allow for flawless 3D data exchange; FBX and Collada support asset instancing, multiple UV-sets, grouping and binary encoding; furthermore, scenes can also be exported to RenderMan RIB or Mental Ray MI format. Textures can be collected during (batch) export;  Python Allows streamlining repetitive or pipeline-specific tasks with the integrated Python scripting interface (e.g. write out arbitrary meta-data or instancing information for each building, import FBX cameras, etc...). CityEngine is also available for Windows (32/64 bits), Mac OSX (64 bits), and Linux (32/64 bits). Fig. II-3 – CityEngine IDE and the Rule Editor Capabilities12
  47. 47. 2.1.3 Bentley – MicroStation Extension: GenerativeComponents (GC)Designers have (since the dawn of times), wanted to innovate. Indeed, innovation is widelyregarded as a trophy that awaits creative professionals who successfully explore endlessdesign alternatives to ultimately arrive at the most efficient solution - a process that can beincredibly time consuming as each alternative is thoroughly modeled and assessed. Usingthe existing tools, a minor change to a design may require a major update to the model,thus restricting the number of design alternatives considered by the team due to timeconstraints. GenerativeComponents is an associative parametric modeling system used byarchitects and engineers to automate design processes and accelerate design iterations. Asan innovation by MicroStation, GenerativeComponents extends proven technologies anddelivers significant advantage to users as they rapidly explore a broad range of designalternatives. With a hybrid approach, designers who use GenerativeComponents can modelgeometry, capture relationships, and generate forms using scripts and/or directmanipulation for unrivalled creative flexibility [7]. Fig. II-4 – GenerativeComponents (GC) IDE, Bentley MicroStationThis combination of accelerated iteration, flexible modeling, and automated process,means that a GenerativeComponents design can be highly efficient, benefiting from acombination of intuition and logic [7]. 13
  48. 48. 2.1.4 Rhinoceros and GrasshopperRhino (a.k.a. Rhinoceros) is a stand-alone, commercial NURBS-based 3D modeling tool,developed by Robert McNeel & Associates. The software is commonly used for industrialdesign, architecture, marine design, jewelry design, automotive design, CAD / CAM, rapidprototyping, reverse engineering as well as the multimedia and graphic design industries. Fig. II-5 – Rhinoceros is used in multiple fields, including architectureRhino is specialized in free-form non-uniform rational B-spline (NURBS) modeling. Plug-ins developed by McNeel includes Flamingo (retrace rendering), Penguin (non-photorealistic rendering), Bongo and Brazil (advanced rendering). Over one hundred third-party plugins are available. There are also rendering plug-ins for Maxwell Render, V-rayand many other engines. Additional plugins for CAM and CNC milling are available aswell, allowing for tool path generation directly in Rhino. Like many other modelingapplications, Rhino also features a scripting language, based on the Visual Basic languageand an SDK that allows reading and writing Rhino files directly. Rhino 3D gained itspopularity in architectural design in part because of the Grasshopper plug-in forcomputational design. Many new avant-garde architects are using parametric modelingtools, like Grasshopper. Rhinos increasing popularity is based on its diversity, multi-disciplinary functions, low learning-curve, relatively low cost, and its ability to import and14
  49. 49. export over 30 file formats, which allows Rhino to act as a “converter” tool betweenprograms in a design workflow. The combination between Rhino and Grasshopper is justperfect to create all kinds of parametric studies and developments on any field. The poweroffered by Rhino and Grasshopper is just amazing. There are also many other pluginsavailable (rendering, math, physics, kinematics…).Fig. II-6 – Voronoi Examples, created using Rhinoceros and Grasshopper, by Atsuo Nakajima (Tokyo, Japan)For designers who are exploring new shapes using generative algorithms, Grasshopper is agraphical algorithm editor tightly integrated with Rhino‟s 3D modeling tools [8]. In Fig.II-7, by using Grasshopper, a building and its structural supports are generated andcalculated using only two splines created initially in Rhino. Fig. II-7 - Parametric Strategies achieved using Rhino and Grasshopper. (Created by the author of this master thesis)Unlike RhinoScript, Grasshopper requires no knowledge of programming or scripting, butstill allows designers to build form generators from the simple to the remarkable [8]. 15
  50. 50. 2.1.5 Generative Modeling Language (GML)Traditionally, 3D objects and virtual worlds are defined by lists of geometric primitives:cubes and spheres in a Constructive Solid Geometry (CSG) tree, NURBS patches a set ofimplicit functions, a soup of triangles, or just a cloud of points.The term “generative modeling” describes a paradigm change in shape description, thegeneralization from objects to operations: A shape is described by a sequence ofprocessing steps, rather than just the end result of applying operations. Shape designbecomes rule design. This approach is very general and it can be applied to any shaperepresentation that provides a set of generating functions, the “elementary shapeoperators”. Its effectiveness has been demonstrated, e.g., in the field of procedural meshgeneration, with Euler operators as complete and closed set of generating functions formeshes, operating on the half-edge level [9].Fig. II-8 - Creation of a simple house model using GML, the extrude operator is repeatedly applied tothe ground polygon. To create the roof, the combined operator collapse-mid is applied to the faceCW and faceCCW edges of the edge returned by the extrude operation.Generative modeling, gains its efficiency through the possibility to create high-level shapeoperators from low-level shape operators. Any sequence of processing steps can begrouped together to create a new “combined operator”. It may use elementary operators, aswell as other combined operators. Concrete values can easily be replaced by parameterswhich makes possible the separation of data from operations: the same processingsequence can be applied to different input data sets. Data can be used to produce differentshapes by applying different combined operators, from, e.g., a library of domain-dependentmodeling operators. This makes possible the creation of very complex objects from only afew high-level input parameters, such as, a style library [2].16
  51. 51. Fig. II-9 – Parameterization/Configuration of a Chair with GMLGML is a concrete implementation of the generative approach. Its main feature is that it isa full functional programming language that can nevertheless be efficiently used as a fileformat for low-level shape descriptions. Only 25 Kilobytes GML code of a Gothic windowstyle library are sufficient to generate connected manifold control meshes for a variety ofwindows [10]. Fig. II-10 – Gothic Style Building generated with GMLThe GML comes with an integrated visualization engine. Thus, it can also be seen as aviewer with an integrated modeler that overcomes the usual separation of 3D modelingfrom interactive visualization. Curved parts are represented as subdivision surfaces that,within 1-2 seconds, unfold to seven million vertices after four steps of recursiverefinement. The surface is adaptively displayed at interactive rates using optimizedmethods for culling and per-face per-frame multi-resolution rendering [11]. Fig. II-11 – Configuration of Different Wheel Rim Styles using GML 17
  52. 52. 2.1.6 Euclides Framework and JavaScriptEnabling an easy access to programming languages that are usually difficult on a directapproach will dramatically potentiate their use. GML [9] is such a language and can bedescribed as being similar to Adobe‟s PostScript. A major drawback of all PostScriptdialects is their unintuitive reverse Polish notation, which makes both - reading and writing- a burdensome task. According to Strobl, M., et al. [12] a language should offer astructured and intuitive syntax in order to increase efficiency and avoid frustration duringthe creation of code. To overcome this issue, Strobl, M., et al. [12] propose a new approachto translate JavaScript code to GML automatically. Within the last few years generativemodeling techniques have gained attention especially in the context of cultural heritage.Because a generative model describes a rather ideal object and not a real one, generativetechniques are a basis for object description and classification. This procedural knowledgediffers from other kinds of knowledge, such as declarative knowledge, in a significant way.It can be applied to a task. This similarity to algorithms is reflected in the way generativemodels are designed: they are programmed. In order to make generative modelingaccessible to cultural heritage experts, Schinko, C., et al. [13] created a generativemodeling framework which accounts for their special needs. The result is a generativemodeler called Euclides based on an easy-to-use scripting language (i.e. JavaScript). Thegenerative model meets the demands on documentation standards and fulfills sustainabilityconditions and its integrated meta-modeler approach makes it independent from hardware,software and platforms.Fig. II-12 – Example of a 3D application created for this thesis using Euclides and JavaScript. It allows the control of several shape parameters on the “Classic Building form example”.18
  53. 53. 2.1.7 Autodesk Revit Architecture 2012In the latest years, Autodesk made a strong effort in incorporating new technologies (e.g.multitouch…) and new amazing functionalities (physics, energy analysis, parametricdesign) in existing products like Autodesk Revit 2012 or Maya 2012, making a strongcontribution for the development of really innovative products.Autodesk Revit Architecture can be used to create massing designs; explore designalternatives based on qualitative and quantitative feedback; and help address variousenvironmental, constructability, and aesthetic concerns that can arise during projectrealization [14]. Fig. II-13 – Energy Consumption Study using Autodesk RevitIn the early stages of a design, visualizing a concept in 3D enhances a designer‟s ability tocommunicate ideas. Analyzing these ideas yields the ability to predict and optimize thereal-world performance of the built project.These attributes form a core value of the Building Information Modeling (BIM) process,which Revit Architecture software is purpose-built to support. 19
  54. 54. In Autodesk Revit Architecture, users have access to a robust collection of easy-to-usemodeling tools that facilitate design conceptualization, visualization, and communication.This software supports several new modeling operations, including adaptive, component-driven geometry, robust UV grid manipulation and increased schedule functionalitythrough reporting parameters. In addition, Revit users on Autodesk Subscription can nowaccess tools that enable them to better assess the impact of their early design decisions onenergy consumption and carbon emissions without leaving the Revit environment. In orderto clearly illustrate a complete workflow using the conceptual design and analysis toolsand to address the new features introduced with the previous release [14]:  The Project requirements section outlines the criteria that will drive the building design;  The Parametric Massing Design section describes the steps taken to explore massing design alternatives informed by qualitative and quantitative feedback;  The Site and Environmental Analysis section addresses the impact of building mass and orientation on energy consumption and overshadowing;  The custom “Panelization” section uses the mass design options generated in the first section as the basis for informed panel‟s studies. Fig. II-14 – Conceptual Design in Autodesk Revit Architecture20
  55. 55. 2.1.8 Project Vasari and Project NucleusAutodesk Project Vasari is an easy-to-use, expressive design tool for creating buildingconcepts and it‟s build on the same technology as the Autodesk Revit platform.Project Vasari goes further, with integrated analysis for energy and carbon, providingdesign insight where the most important design decisions are made. And, when it‟s time tomove the design to production, simply bring your Vasari design data into theAutodesk Revit platform for BIM, ensuring clear execution of design intent.Project Vasari is still under development and is primarily intended to reduce the buildingenergy loads, not to replace the more detailed analysis tools. It is able to produceconceptual models using both geometric and parametric modeling functionality. Thedesigns can be analyzed using the built-in energy modeling and analysis features. The toolsdepends on Green Building Studio (Autodesk‟s green building analysis web service) inmany input energy related parameters [15]. Fig. II-15 – Sun Studies using project “Vasari”Project Vasari is focused on conceptual building design using both geometric andparametric modeling. It supports performance-based design via integrated energy modelingand analysis features. This new technology preview is now available as a free downloadand trial on Autodesk Labs. 21
  56. 56. Project Nucleus integrates the Nucleus simulation engine from Autodesk Maya intoAutodesk Revit Architecture and Project Vasari. It allows designers to experiment with"form-finding" in the conceptual design phase by simulating forces directly in RevitArchitecture and Project Vasari (the latest technology preview of Project Vasari alreadyincludes the Project Nucleus functionality). Fig. II-16 - Panel Study using Revit and VasariProject Nucleus can simulate a wide range of physical phenomena in real time, like wind,gravity, constraints, and collisions. These forces can help architects generate free-formshapes, many of which would be impossible to model by hand [16]. Fig. II-17 - Using Revit, Vasari and Nucleus Physics for a Panel Study, plus Analysis22
  57. 57. 2.1.9 Autodesk Adaptive ComponentsAdaptive geometry can be sized and positioned in the context where it is used. When youdesignate under constrained geometry as adaptive, you specify the geometric elementsallowed to change, while controlling the elements that you want to remain a fixed size orposition [17].“Adaptivity” is the functionality, within Inventor, that allows the size of a part/feature to bedetermined by setting a relationship to another part in an assembly. Basically, “adaptivity”is a special way to add constraints. These constraints differ from regular constraints in thatthey are driven from a separate file. This separate file can be an assembly file or anotherpart within the assembly file.A good example of “adaptivity“, is constraining a shaft to a hole in another part. If set upcorrectly, when the size of the hole changes the diameter of the shaft updates as well.“Adaptivity” is normally used during the initial design phase of a model, when changes aremade rapidly and many parts are affected. Fig. II-18 – Adaptive Components in Autodesk Revit 23
  58. 58. Once a design is released, and parts become standard parts, available for use in otherdesigns, “adaptivity” should be removed to eliminate the possibility of inadvertentlychanging a released design. Removing “adaptivity” also improves performance. Fig. II-19 – Adaptive Panel example in Autodesk RevitAs with using any other constraint, forethought should be given to how a design maychange before “adaptivity” is applied. If a part is not likely to change, it is better to applynormal (non-adaptive) constraints. “Adaptivity” should be used only when absolutelynecessary [17]. Fig. II-20 – Another Adaptive Panel example, built using Adaptive Components24
  59. 59. 2.2 Evolutionary Architecture and the Use of Algorithms in Optimization of ProblemsThe first references to this field of computation, Evolutionary Solvers or GeneticAlgorithms [18], can be found in the early 60s when Lawrence J. Fogel published therevolutionary paper "On the Organization of Intellect" [19] which steered the firstendeavors into evolutionary computing. The early 70s saw further ventures with importantwork produced by Ingo Rechenberg and John Henry Holland (and others) [20].Evolutionary Computation didnt gain popularity beyond the programmer world untilRichard Dawkins (one of my favorite authors) came out with the book, "The BlindWatchmaker" in 1986 [21], which was published with a small program that generated anapparently endless stream of body-plans called "Bio-morphs" based on human selection. Fig. II-21 – Image taken from the book “The Selfish Gene” by Richard DawkinsAfter the 80s, the dawn of the personal computer has made it possible for individualswithout government funding to apply evolutionary principles to personal projects andmaking it a common jargon. The term "Evolutionary Computing" is very well commonlyknown at this time, but is still very much a programmer‟s tool (by programmers and forprogrammers) [18, 22]. 25
  60. 60. The applications out there that apply evolutionary logic are either aimed at solving specificproblems or they are generic libraries that allow other programmers to develop their ownsoftware [21]. Fig. II-22 – Several visions related to Evolutionary Architecture and BiomimeticOne of the most important works ever published published about EvolutionaryArchitecture was the book of John Fraser, “An Evolutionary Architecture” [23], in thebook introduction one can read: “…in this book the author investigates the fundamentalform-generating processes in architecture, considering architecture as a form of artificiallife, and proposing a genetic representation in a form of DNA-like code-script, which canthen be subject to developmental and evolutionary processes in response to the user and theenvironment. The aim of an evolutionary architecture is to achieve in the builtenvironment, the symbiotic behavior and metabolic balance found in the naturalenvironment. To do so, it operates like an organism, in a direct analogy with the underlyingdesign process of nature”. Fig. II-23 – Evolutionary examples taken from the book “An Evolutionary Architecture” by John Fraser [23]26
  61. 61. Also, Gordon Pask wrote on his foreword on this same book: “The book also proposes afundamental change in practice… „The role of the architect here, I think, is not so much todesign a building or city as to catalyze them: to act that they may evolve‟. Promisingsustainable design methods are unquestionably emerging through the use of evolutionarycomputation and environmental simulation tools, as this is indeed an essential need intoday‟s architecture world”.Fig. II-24 – Dynamic Geometry Computation, “Shanghai Tower - Geometry Generate and Rendering” (Michael Peng)Eugene Tsui on his work and book “Evolutionary Architecture: Nature as a Basis forDesign” [24] and also Javier Senosiain, Michael Paulin, William McDonough, RenzoPiano and many others architects incorporate in their projects ecological and sustainableprinciples, but also integrate an understanding that constructions require “an holisticapproach studying the form, materials and efficiency that Nature have becoming theinfallible mentor in the creation of an comfortable and symbiotic world” [25]. Fig. II-25 – Ecological House of the Future (Eugene Tsui) 27
  62. 62. 2.2.1 Differential Evolution (DE)Differential Evolution (DE) [26] has been very successful in solving the global continuousoptimization problem [27]. It mainly uses the distance and direction information from thecurrent population to guide its further search. The global optimization problem arises inalmost every field of science, engineering or business, and an enormous amount of efforthave been devoted to solving this problem. The major challenge of the global continuousoptimization is that the problems to be optimized may have many local optima (Sun, J., etal. [27]). Fig. II-26 – The Global Optimization Problem (example in Matlab). Search of the highest mountain peak among a neighborhood of other high mountains peaks.Evolutionary Algorithms (EA‟s) are similar to the evolution process of a biologicalpopulation which can adapt to the changing environments in order to find the optimum ofthe optimization problem by evolving a population of candidate solutions. DifferentialEvolution (DE) is one of the most successful EAs for the global continuous optimizationproblem. Several examples of problem solving using DE were already presented in thepast, particularly those ones presented by the creators of the DE algorithm (Price, K. S., etal., [28]).28
  63. 63. Evan Greenberg [29] discusses in is master thesis a natural behavior called “Branching”that occurs in natural systems for functional reasons. The branching logic for each specificsystem is quite different due to environmental and mathematical factors. In thecomputation of branching systems, these mathematical factors can be incorporated easilyinto the coding of each system. Nevertheless, the environmental components deservefurther consideration in the simulation of these natural systems. Through the engine ofgenetic algorithms based on evolutionary developmental theory, the specific logicsobserved and analyzed in branching patterns of river systems or trees, can be simulated andoptimized in a digital environment. Fig. II-27 - Observation, Analysis and Computation of Branching Patterns in Natural Systems (by Evan Greenberg [29])There are some biological terminologies which are used in evolutionary algorithmimplementations, such as:  Individual: an autonomous piece characterized by a chromosome. In this case, one possible solution to the design problem;  Population: a group of individuals;  Population Size: the number of individuals in a population Gene (a functional block of DNA); 29
  64. 64.  Allele: A possible value of a gene;  Chromosome: Strings of DNA. In this case, a list of parameters;  Locus: The place of a gene in a chromosome.In evolutionary algorithms there are also three different types of operators: Selection,Crossover, and Mutation. After initializing the parameters, these three operators areiterated until the results satisfy the terminal criteria defined.Each step of the algorithms is explained as follows (Kawakita, G. [30]):  Initialization - In this step, some parameters including the population size, number of generations and so on are entered. After that, the initial input randomly generates genotype individuals of the first generation. Particularly, population size is significant in terms of the operations, the lengthier the chromosome length is, the bigger the population size is. Additionally, a bigger population size requires longer calculation time until convergence. However, small population sizes may result in premature and undesirable convergence;  Evaluation - Fitness scores are calculated for further selection of fitter chromosomes. One of the most important aspects in this step is the Fitness Function which calculates the fitness measurement of each individual. This operation is deeply related to the efficiency of the whole Genetic Algorithm (GA) flow; therefore, it needs to be determined carefully;  Selection - The fitter chromosomes in the population are basically selected for reproduction. As in biological evolution, the fitter chromosomes are more likely to be selected and reproduced in each generation. Meanwhile, lower fitness chromosomes are also possibly selected, but with a lower probability. This probabilistic selection depends on the selection method. There are several types of selection such as elite selection, roulette selection, tournament selection, etc. Each selection type has advantages and disadvantages. For instance, in elite selection, the30
  65. 65. fitter chromosomes are certainly selected in order; however, premature convergence is highly possible; Crossover - Crossover roughly mimics the genetic operation of biological recombination between two chromosomes. The fitter chromosomes are chosen by the selection operator. However, it is not effective enough to evolve the population. The crossover operator encourages more variation by exchanging genes between two chromosomes; Mutation - The mutation operator randomly flips or changes genes in a chromosome between alleles, generally with a very low probability. Chromosomes generated by the crossover operator are basically copies of the parent chromosomes. Therefore, premature convergence possibly occurs. Chromosomes that have been mutated help to avoid premature convergence. Generally speaking, the mutation rate should be 1/L, where L is the length of chromosome. Moreover, if the mutation rate is too big, the algorithm becomes similar to a random search; Terminal Criterion - In this step, the conditions required to terminate the algorithm is evaluated. If the process is regarded as being completed, the fittest individual in the generation is outputted as one of the possible optimum solutions.The general conditions of convergence in evolutionary algorithms are as follows:  If the fittest score in the population satisfies the certain target – star gene;  If the average fitness score in the population satisfies the certain target – population improvement;  If the increase or decrease of fitness scores in the population becomes below a certain value – convergence;  If the number of generations becomes over the defined value – finite iteration. 31
  66. 66. Fig. II-28 – Simple EA’s stepsAs it happens with every algorithm, there are several different variations of the differentialalgorithm, in order to classify the different variants, the notation: DE/x/y/z was introduced,where:  x specifies the vector to be mutated which can be “rand” (a randomly chosen population vector) or “best” (the vector of lowest cost from the current population);  y is the number of difference vectors used;  z denotes the crossover scheme. Example: “bin” (Crossover due to independent binomial experiments).Using this notation, the basic DE-strategy that is generally described can be written as:DE/rand/1/bin, but there are other variants, e.g. DE/best/2/bin.32
  67. 67. The DE algorithm work‟s (in general) as follows (Price, K. S., et al.[28]): 1. The DE algorithm maintains a population of N points in every generation, where each point is a potential solution and N is a control parameter; Then the algorithm evolves and improves the population iteratively: 2. In each generation, a new population is generated based on the current population; 3. To generate descendants for the new population, the algorithm extracts distance and direction information from the current population members and adds random deviation for achieving diversity; 4. If an offspring has a lower objective function value than a predetermined population member, it will replace this population member; 5. This evolution process continues until a stopping criterion is met (e.g., the current best objective function value is smaller than a given value or the number of generations is equal to a given maximum value). Fig. II-29 – General Evolutionary Algorithm: i: initialization, f(X): evaluation, ?: stopping criterion,Se: selection, Cr: cross-over, Mu: mutation, Re: replacement, X*: optimum. Author: Johann "nojhan" Dréo 33
  68. 68. The optimization method known as Differential Evolution (DE) has several parameters thatdetermine its behavior and efficacy in optimizing a given problem. The selection of goodparameters for DE it is an important question that is discussed by Pedersen, M. [31], thispaper gives a list of good possible choices of parameters values for various optimizationscenarios with the intention to give an easy help when choosing the best values forachieving the best results, these interrelated parameters are: Crossover (CR), usually agood initial value for CR would be 0.9 or 1.0 to check if a quick solution is possible,Number of evaluations (Fitness Evaluations), Number of elements in each generation (NP),a good value for NP is between 5*D (D = Dimension of the problem) and 10*D, but NPmust be at least 4 to ensure that DE will have enough mutually different vectors to whichto work on, Differential Weight (F), a good initial value for F is usually 0.5, Number ofvariables of the problem (Problem Dimensions) and Size of the domain for each variable ofthe problem. Fig. II-30 - DE optimization performance (Pedersen, M. [31]) on several different problems using DE/rand/1/bin algorithm. Plots show the mean fitness achieved. Over 50 optimization runs34

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