Autorentext

Dr Victor Okhoya holds a Doctor of Design in Architecture from Carnegie Mellon University with a specialty in Computational Design, and has worked with architectural computational technology for more than twenty years. He has published widely on architectural computation technology and related subjects, has held leadership roles in digital design technology at leading architectural practices, and has trained hundreds of industry professionals on the use of digital design tools. Marcelo Bernal, PhD, holds a doctoral degree in Architecture from the Georgia Institute of Technology with a specialty in Computational Design, and has more than two decades' experience in research and architectural practice. He has held professorial positions at the Universidad Techica Federico Santa Maria (UTFSM), Chile, and has published widely on architectural design and related subjects.

Klappentext

Let these cutting-edge tools revolutionize your architectural problem-solving Recent technological advances, including rapid development of high-performance computing power, have produced computational tools for architects and designers that are swiftly revolutionizing the field. Two of these tools, parametric analysis and generative design, can be used together to model complex problems and test solutions with extraordinary speed and efficiency. The next generation of architects and designers will bring these tools to bear in a transformed professional landscape. Parametric Analysis and Generative Design in Architectural Practice offers a detailed introduction to these tools and their applications. Balancing theory and practice, it is replete with useful examples and provides algorithms for three different architectural scales: urban design, building design, and interior design. A foundational discussion on machine learning in architectural practice is presented, along with examples of machine learning applications for energy analysis and solar analysis. It is a must-own for architects looking to leverage an unprecedented level of computational power. The book also includes:

• Detailed guidelines for building performance simulation for energy analysis and daylight analysis
• Algorithms for scales from interiors to buildings to entire cityscapes
• Discussion of how to implement machine learning in order to solve complex problems Parametric Analysis and Generative Design in Architectural Practice is ideal for professionals and students at every level looking to diversify their architectural toolkit.

Inhalt

Preface xi

Acknowledgments xv

Part One: Back ground and Definitions 1

Chapter 1

COMPUTER-AIDED DESIGN AND BUILDING INFORMATION MODELING 3

Computer Aided Design 5

Building Information Modeling 9

Chapter 2

COMPUTATIONAL DESIGN AND PARAMETRIC DESIGN 19

Computational Design 19

Parametric Design 30

Chapter 3

WHAT IS PARAMETRIC DESIGN? 41

Definition of Parametric Design 41

The Need for Parametric Design 47

Project Case: Parametric Analysis 51

Project Case: Generative Design 60

Part Two: Parametric Analysis 73

Chapter 4

METHODS OF PARAMETRIC ANALYSIS 75

Performing Energy Simulations 75

Performing Daylight Simulations 82

Daylight Analysis Process 83

Performing Parametric Analysis 90

Evaluating Parametric Analysis 97

Chapter 5

PARAMETRIC ANALYSIS FOR ENERGY AND DAYLIGHT PERFORMANCE 105

Energy Analysis Project Case: Hotel Residence 105

Project Case: Student Housing 112

Project Case: Student Residence 120

Project Case: High School Building 132

Part Three: Generative Design 139

Chapter 6

METHODS OF GENERATIVE DESIGN 141

Space Partitioning Algorithms 146

Site Subdivision Algorithms 155

Form Making Algorithms 160

Chapter 7

GENERATIVE DESIGN AT THE URBAN DESIGN SCALE 171

Project Case: Business Park 172

Project Case: Mixed Housing 179

Chapter 8

GENERATIVE DESIGN AT THE BUILDING SCALE AND INTERIOR DESIGN SCALE 195

Generative Design at the Building Scale 195

Generative Design at the Interior Design Scale 206

Part Four: Machine L earning 219

Chapter 9

MACHINE LEARNING FOR ENERGY ANALYSIS 221

Machine Learning Algorithms 223

Project Case: Residential Tower 230

Chapter 10

DEEP LEARNING FOR SOLAR ANALYSIS 239

Types of Deep Learning Algorithm 241

Generative Adversarial Networks 246

Project Case: Urban Solar 252

Bibliography 257

Index 259