The bright future and exciting possibilities of BIM

Many architects and engineers regard BIM as a disruptive force, changing the way building professionals design, build, and ultimately manage a built structure. With its emphasis on continuing advances in BIM research, teaching, and practice, Building Information Modeling: BIM in Current and Future Practice encourages readers to transform disruption to opportunity and challenges them to reconsider their preconceptions about BIM.

Thought leaders from universities and professional practice composed essays exploring BIM's potential to improve the products and processes of architectural design including the structure and content of the tools themselves. These authors provide insights for assessing the current practice and research directions of BIM and speculate about its future. The twenty-six chapters are thematically grouped in six sections that present complementary and sometimes incompatible positions:

• Design Thinking and BIM
• BIM Analytics
• Comprehensive BIM
• Reasoning with BIM
• Professional BIM
• BIM Speculations

KAREN M. KENSEK and DOUGLAS E. NOBLE teach at the University of Southern California, School of Architecture. Prof. Kensek has received national BIM honors from the AIA TAP committee and Autodesk, hosts an annual conference on Building Information Modeling, and received the 2014 ACSA Award for Creativity with Prof. Noble. They are both past presidents of Association for Computer Aided Design In Architecture (ACADIA) and are active in the American Institute of Architects (AIA).

Foreword xvii

Acknowledgments xxi

Introduction xxiii

Software Mentioned xxxi

Part 1 Design Thinking and BIM 1

Chapter 1 Smart Buildings/Smart(er) Designers: BIM and the Creative Design Process
Glenn Goldman Andrzej Zarzycki

1.1 Introduction 3

1.2 Evaluation of Visual Information: Form 5

1.3 Generative Abilities of Parametric Models 6

1.4 How Lighting, Thermal, and Structural Considerations Can Drive the Design 6

1.5 Limitations of Current Parametric Models 8

1.6 Physics and Materiality 9

1.6.1 Solving for Multiple Criteria 10

1.6.2 Other Data Types 10

1.6.3 Soft Constraints 11

1.7 Design and Construction 2.0 12

1.7.1 Context-Aware Data 12

1.7.2 Beyond a Single Lifespan of the Project 13

1.8 Conclusion 15

Discussion Questions 15

Bibliography 16

Chapter 2 Necessity of Cognitive Modeling in BIM's Future 17
Ömer Akin

2.1 Introduction: Some Useful Concepts 17

2.2 Building Information Modeling: The Brand New World of Design Computing 20

2.3 Cognitive Strategies for BIM: Challenges and Opportunities 21

2.4 Conclusions 26

Discussion Questions 26

References 27

Chapter 3 Modeling Architectural Meaning 29
Mark J. Clayton

3.1 Introduction 29

3.2 Architectural Ontology 30

3.3 Regulating Lines 30

3.4 Diagrams and Semantics 36

3.5 Types 38

3.6 Conclusion 40

Discussion Questions 40

References 41

Chapter 4 Knowledge-Based Building Information Modeling 43
Hugo Sheward Charles Eastman

4.1 The Potential of Building Information Modeling (BIM) to Capture Design Expertise 43

4.2 "Vanilla BIM" versus Knowledge-Based BIM 44

4.3 What Is Design Expertise? 44

4.3.1 Heuristics Applied to Design Processes 45

4.3.2 Design Workflows and Knowledge-Based BIM 46

4.4 Capturing and Deploying Design Expertise 47

4.4.1 Capturing Design Expertise 47

4.4.2 Embedding Knowledge in BIM 47

4.4.3 Example 1: Building Service Core 49

4.4.4 Example 2: Ventilation in Laboratories 50

4.5 Examples of Deployment 53

4.5.1 Deployment in Manufacturing 53

4.5.2 Uses in Architecture, Engineering, and Construction 53

4.6 Summary 54

Discussion Questions 54

References 55

Part 2 BIM Analytics 57

Chapter 5 Parametric BIM SIM: Integrating Parametric Modeling, BIM, and Simulation for Architectural Design 59
Wei Yan

5.1 Executive Summary 59

5.2 Introduction 59

5.2.1 Parametric Modeling 60

5.2.2 BIM and Parametric BIM 60

5.2.3 Building Energy Simulation 61

5.2.4 A Streamlined Modeling Process 63

5.3 Complexity and Interfaces 65

Chapter 6 Models and Measurement: Changing Design Value with Simulation, Analysis, and Outcomes 79
Phillip G. Bernstein Matt Jezyk

5.3.1 Complexity and Computability 65

5.3.2 User Interfaces and System Interfaces 66

5.4 Case Studies 69

5.4.1 Physical BIM for Thermal and Daylighting Simulations 69

5.4.2 Parametric BIM-Based Energy Optimization 72

5.5 Conclusion 74

Acknowledgments 74

Discussion Questions 74

References 75

6.1 Introduction 79

6.2 BIM 1.0 80

6.3 Analysis and Simulation through BIM 1.0 80

6.4 BIM 2.0 83

6.5 Geometry, Behavioral Properties, Parameters, and Analysis 85

6.6 Ideation and Design Production under BIM 2.0 89

6.7 Design Empowerment 91

6.8 Conclusion: Avenues to Alternative Value Generation 91

Discussion Questions 92

References 93

Chapter 7 Energy Modeling in Conceptual Design 95
Timothy Hemsath

7.1 Introduction 95

7.2 Building Performance Simulation (BPS) 95

7.3 BIM's Role in the Process 97

7.4 Conceptual Design Decisions 98

7.5 Sensitivity Analysis and Optimization 101

7.5.1 Sensitivity Analysis 101

7.5.2 Conceptual Design Optimization 102

7.6 BIM Affordances 105

7.7 Conclusion 107

Acknowledgments 107

Discussion Questions 107

References 108

Chapter 8 Performance Art: Analytics and the New Theater of Design Practice 109
Daniel Davis Nathan Miller

8.1 Introduction 109

8.2 Instruments 110

8.3 Analytics 112

8.4 Interactions 115

8.5 Conclusion: Algorithms Are Thoughts 116

Discussion Questions 117

References 117

Chapter 9 Automated Energy Performance Visualization for BIM 119
Paola Sanguinetti Pasi Paasiala Charles Eastman

9.1 Introduction 119

9.2 Case Study: Automated Analysis of U.S. Courthouse Models for GSA 120

9.2.1 Preliminary Concept Design (PCD) 120

9.2.2 Post-Processing for Energy Analysis 120

9.2.3 Building Model Property Definition 123

9.3 Performance Visualization 123

9.3.1 Aggregation of Simulation Output Variables 124

9.3.2 Visualization of Thermal Flows 124

9.4 Discussion 125

9.5 Conclusion 127

Acknowledgments 127

Discussion Questions 127

References 127

Chapter 10 Urban Energy Information Modeling: High Fidelity Aggregated Building Simulation for District Energy Systems 129
Nina Baird Shalini Ramesh Henry Johnstone Khee Poh Lam

10.1 Introduction 129

10.2 Understanding District Energy Systems 129

10.3 Community Energy Planning 130

10.4 Dynamic Energy Mapping 132

10.4.1 An Initial Example: Pittsburgh's Lower Hill District 132

10.4.2 Urban Energy Simulation of the Lower Hill District 133

10.4.3 Future Improvements Using Cloud Services 134

10.4.4 First Order District System Analysis 135

10.4.5 Data Visualization for Time-of-Use Aggregate Load Profiles 136

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