Design Informed E-Book

Gordon H. Chong

• Herausgeber: John Wiley & Sons
• Kategorie: Geisteswissenschaft
• Sprache: Englisch

Seitenzahl: 549

Veröffentlichungsjahr: 2010

Beschreibung

The Power of Evidence to Create Design Excellence This practical, accessible book--for design professionalsand students alike--is about design excellence and how toachieve it. The authors propose an evidence-based design approachthat builds on design ingenuity with the use of research in waysthat enhance opportunities to innovate. They show the power ofresearch data to both reveal new design opportunities and convincestakeholders of the value of extraordinary work. A guide for alldesigners who want to earn their place as their clients' trustedadvisor and who aspire to create places of beauty and purpose, thebook demonstrates: * An approach to applying evidence to design that neither turnsdesigners into scientists nor requires large-firm resources * The wide range of types of evidence that can be applicable todesign and where to look for it * Direct, practical application of the evidence-based designapproaches in use today * Provides tools to distinguish strong evidence that can improvedesign decisions from misleading assertions resulting from weakresearch * Benefits of evidence-based design, including improved human andbuilding performance Two featured case studies illustrate the theory and practice ofevidence-based design. The work of the authors' 2005-2007 AIACollege of Fellows Benjamin Latrobe Research Fellowship provided anempirical foundation for this book, and addresses the use ofrigorous research methods to understand relationships betweendesign choices and health outcomes. The California Academy ofSciences, designed by Renzo Piano Building Workshop, Chong PartnersArchitecture, and Arup, provides transparent evidence that enhancesbuilding technology performance in the context of a powerful designexpression. In-depth interviews and case studies are clustered around threeresearch categories: modeling, simulation, and data mining; socialand behavioral science and the physical and natural sciences; andincluding cutting-edge use of neuroscience to understand humanresponse to physical environments. The twenty-two featured thoughtleaders include: William Mitchell, MIT Media Lab; FredGage, Salk Institute; Phil Bernstein, Autodesk;Sheila Kennedy, Kennedy & Violich; JamesTimberlake, KieranTimberlake; William and ChrisSharples, SHoP Architects; Vivian Loftness, CarnegieMellon University; John Zeisel, Hearthstone; PacoUnderhill, Envirosell; Susan Ubbelohde and GeorgeLoisos, Loisos+Ubbelohde Architecture-Energy; ChrisLuebkeman, Arup; Martin Fischer, Stanford UniversityCIFE; and Kevin Powell, GSA.

Leseprobe

Table of Contents

Title Page

Copyright Page

Preface

Acknowledgments

Chapter 1 - Transformation

Not As It Seems

Time for a Makeover

What Will It Look Like?

The Authors’ Journey

The Road Well Taken

Chapter 2 - Models, Simulation, and Data Mining

Background and Context

Interviews of Experts and Case Studies

Lessons Learned: Models, Simulation, and Data Mining

Chapter 3 - The Social Sciences

Background and Context

Interviews of Experts and Case Studies

Lessons Learned

Chapter 4 - The Natural and Physical Sciences

Background and Context

Interviews of Experts and Case Studies

Lessons Learned

Chapter 5 - Putting It All Together

Background and Context

The Primary Research Methodologies Utilized

Chapter 6 - The 2005 Latrobe Fellowship

Introduction

Process Model and Methodologies

Literature and Experimental Findings

Credits

Chapter 7 - Applying What We’ve Learned

What Does This Mean for Design Practice?

Expanded Horizons

Strength of Evidence

The Right Methodology and the Right Metrics

Where Do We Go from Here?

How?

The Most Fruitful, Near and Long-Term Areas for the Application of ...

A Final Word

Index

This book is printed on acid-free paper.

Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and the author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor the author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

For general information about our other products and services, please contact our Customer Care Department within the United States at [phone], outside the United States at [phone] or fax [phone].

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. For more information about Wiley products, visit our web site at www.wiley.com.

Library of Congress Cataloging-in-Publication Data

Brandt, Robert, 1948-

Evidence-based architectural design : case studies of applied evidence / Robert Brandt, Gordon H. Chong, W. Mike Martin.

p. cm.

Includes index.

ISBN 978-0-470-39562-2 (cloth)

1. Evidence-based design. I. Chong, Gordon H. II. Martin, W. Mike. III. Title. IV. Title: Case studies of applied evidence.

NA2750.B65 2010

720.1—dc22

2009049257

Preface

A QUIET REVOLUTION IS UNDERWAY, one that could change the practice of architecture for years to come. It isn’t being trumpeted at the design awards ceremonies, yet it is about design excellence. It hasn’t been widely embraced by the profession but it is relevant to all design professionals who wish to remain relevant. Corporate and institutional architects and interior designers will only thrive if they know how to create places of long-standing value to their clients and communities. This revolution in the way design is practiced is the means to ensure that level of design quality.

The genius of architects is their ability to imagine building form and then give physical structure to their musings. Architects should never relinquish this mastery of art and technology; it defines them as professionals. The question is how even greater and more sustainable beauty and utility can be created. The revolution that is gently but inexorably changing architecture looks to science as the means to better design outcomes.

Architects and other design professionals typically depend on intuition and personal project experience to make design choices. That works at some level but is limited by the self and the past. New things might be tried but there’s no basis to predict how well they’ll work, if the only criteria come from the designer’s prior experience.

The time has come to move on from this self-limiting approach. Picture this instead:

• Using a computer simulation, you discover a way to reduce your client’s space program by 30%. They reinvest part of the capital budget in an upgraded design and use the rest of the savings to do a project they otherwise couldn’t afford.

• You’ve claimed that you can help your client increase productivity through some creative new design ideas, but they need data to convince their stakeholders to change what they’re used to. By providing compelling evidence to back up your claims, you succeed in getting approvals and move forward with some breakthrough design concepts.

• Bio-medical research connecting daylight and health convinces your client, a hospital administrator, to build a narrower footprint building. You design a place that is enlivened by light and views, instead of an artificially lit, enclosed space. Patients and staff thrive and you’ve aided the healing.

• Prototyping demonstrates innovative ways to use a metal skin. You use the test data to design a unique building form with extraordinary beauty and free expression.

The vision of what might be seems unlimited. Aesthetics, experience, sustainability, cost reduction, improved operations, well being….Designers can break through and do great work. All that’s needed is evidence to understand how specific design strategies might affect building performance. With evidence, we can predict and convince.

Evidence-based design (EBD) is slowly changing how the design is practiced by design professionals and valued by their clients. It can improve the quality of design, especially in ways that benefit clients. However, EBD is also often misunderstood. Many architects think it will be overly prescriptive, rather than informative. Others who like the notion don’t fully grasp how to assess if evidence is strong or weak, and in what contexts the evidence is valid.

This book is about the authors’ journey to find an approach to EBD that will co-exist with design creativity, increase innovation, and lead to improved building performance. Think of this as “Informed Intuition”—a healthy mix of the professional’s instincts and a broad, deep knowledge base from many sources.

Along this journey, the authors encountered a number of experts and asked them to share their experiences and perceptions related to EBD.

1. Is the use of empirical evidence appropriate to design? If yes, under what circumstances and to what benefit? How does the use of evidence in design differ from that in other professions?

2. What constitutes evidence for design? How much is enough and how rigorous does it need to be? What methodologies—qualitative or quantitative—are required for it to be credible and defensible in informing design decisions?

3. What are the appropriate types of evidence and how might they be obtained? Are there successful precedents? What architects are doing it with great outcomes and can others also succeed in spite of time and budget constraints? Does the search for evidence, in lieu of pure instinct, diminish creativity?

4. Will my practice improve if I adopt an evidence-based design approach to my projects? Is it for all types of firms?

Several major themes emerged from this dialogue. Considered together, they describe an approach to EBD that’s both broader and more demanding than much of what’s in the current literature.

There are many sources of data that might serve as evidence of design impacts. Post-occupancy evaluation surveys, often cited in discussions of EBD, is only one method for seeking evidence. Computational, social and natural sciences are rich resources. This book addresses all three.

Strength of evidence (i.e., how much you can rely on the data to predict design impacts on your projects) is often not understood by designers, yet it is critical to applying evidence reasonably. Architecture lacks the research standards and protocols necessary for widespread development, application, and dissemination of research that could serve as evidence. As EBD develops, design education will need to better prepare professionals to appreciate research quality standards and all practitioners will need to hone their capabilities to assess what evidence might be used in making better design choices.

Knowledge gained from an assessment of one project’s performance outcomes might have great potential value for other projects. However, without shared research standards, we can’t tell if that knowledge is of good quality nor if it can be generalized from one project context to another. The design professions also must support knowledge sharing far better. Our current systems to categorize, store, and retrieve data/ knowledge are few and far between.

As our concerns for human response, behavior, and performance become more complex; environmental impacts more important; and fiscal resources more constrained, clients and communities are demanding more understanding of the value of design. Will design professionals be able to make a strong case for high-performing buildings and the ability to use design as a lever to achieve high performance? To remain relevant, architects must…and can with the right evidence to back up these assertions of added value.

This book, one in a series by John Wiley & Sons that explores the concept of evidence-based design, is not about being a researcher; it is about being a better designer and a better architect who uses evidence as one approach to informing design.

The book raises as many questions as it answers but it reveals sources of evidence both internal and external to architectural practice, and addresses how and why to apply them. You won’t find the ten steps to developing an evidenced design practice but you will find ideas that will stimulate your own thinking about the use of evidence in your design practice. The revolution won’t stop but every practitioner can have a voice in shaping how the design professions evolve.

In this book, the authors share some ideas about what the use of evidence might mean for a design practice…how evidence-based design might expand your horizons, bolster innovation, and reposition you to become your client’s trusted advisor. Simply, the book is about where we might take the journey from here with a somewhat different dialogue than we’ve heard before.

This book will help the practicing architect, client, and students of architecture through three types of learning.

1. Background on research methodologies: Intended to help you decide what is most appropriate for your application. Discussion of these methodologies is intended to be absolute but rather provides a broad context of possibilities for your consideration, as you consider your own needs. Our book does not support a single, prescriptive approach.

2. Application examples: Interviews and case studies are intentionally diverse in scale, approach, and research methodology so that you can learn, analyze, pick and choose, and envision how they may apply to your design question, skill, and resource. There is not a case of “one size fits all,” but rather, many approaches from which to choose the most appropriate. The examples illustrate actual use in current project work and specific types of research being conducted for application.

3. Thoughts about the future: When speaking about the use of evidence, many architects are fearful that the process will inhibit creativity. Our observations challenge that fear and open a dialogue about expanded possibilities as architecture joins other valued professions by integrating the best of the traditional intuitive approach with an empiricism that enhances design outcomes.

Acknowledgments

With thanks!

We wish to thank The American Institute of Architects, College of Fellows for awarding the 2005 Latrobe Fellowship to Chong Partners Architecture, the University of California, Berkeley, and Kaiser Permanente. This two-year study formed the genesis of our thinking and permitted the research for this publication to ask a higher level of questions in our search for innovation and excellence.

We also wish to thank the many brilliant individuals who generously shared their thoughts, time, and experiences with us. This publication is a reflection of their many great ideas and thoughts.

Lastly, we thank John Wiley & Sons for inviting us to do this work, which we believe will add to a greater body of professional knowledge for the betterment of the profession and the public we serve.

Special Thanks

With special thanks to Professor Michael Bednar, of The University of Virginia, who encouraged me to believe that Design and Behavior really can be One; Michael Gulash and our colleagues at Intuit, who patiently supported my Work: Book Balance; and my sister, Pamela Robin, who told me I could write, before I could.

ROBERT BRANDT, AIA

With appreciation to John P. Eberhard, FAIA, for introducing me to the possibilities of neuro-architecture as a means to inform design; to The Academy of Neuroscience for Architecture for sustaining that interest and to my wife, Dorian, for encouraging my intellectual curiosity.

GORDON H. CHONG, FAIA

I dedicate this book to my wife, Pat, and our two daughters, Brandi and Cally, for offering hope, encouragement, caring, and love for all things that matter.

W. MIKE MARTIN, FAIA, PHD

1

Transformation

Figure 1.1 Evidence Development and Application

ARCHITECTURE STARTS WITH VISION AND PASSION—VISION OF A PLACE THAT WILL INSPIRE OUR SENSES AND A PASSION TO CREATE IT. THIS BOOK IS ABOUT TAKING THAT SPATIAL, GEOMETRIC, AND AESTHETIC STARTING POINT AND EXPANDING IT TO EMBRACE BUILDING AND HUMAN PERFORMANCE.

The agenda is a TRANSFORMATIVE one. It builds on what architects do best—make form. Our education as architects is dominated by a language of spatial principles: shape, scale, color, texture, pattern, symmetry, balance, accent. These are the things we are taught and should always be central to what we design. The question is “Are they enough?” Our answer is “No!”

This is a very exciting time for our profession. Every day, more evidence is being created that demonstrates the power of architecture to affect human experience and environmental outcomes. Extraordinary innovations in building performance and materials science are now also possible due to evidence-producing processes.

Today’s technologies and challenges feed opportunities to refine, expand, and improve our abilities to make form.

In the transformation we envision (see Figure 1.1), professional practice will still be based on our values and traditions as architects; yet our aspirations and capabilities will go beyond designing only spatially inspirational buildings. In this future, in addition to form-making, design professionals will positively influence human well-being and effectiveness and will contribute to the health of our planet. For this to be our future, our profession must acknowledge that the means to this end is being able to predict design outcomes. We must be able to rely on evidence to anticipate the effects of our work. In order for this information to help us make a high impact, positive design choices must be transparent, accessible, understandable, and applicable. What follows in this book is a journey to define what such evidence might be and how we might develop and apply it.

“EvidenceforDesign” or “EvidenceorDesign?”

During a 2008 interview on National Public Radio, New York Times political commentator David Brooks referred to some of the people being considered for his administration by then President-Elect Barack Obama as being “evidence-based.” This characteristic, according to Brooks, created potential bridges between Obama and people with sometimes divergent opinions. Disciplined consideration of the facts (evidence) enabled them to make reasoned decisions, with the advantage being that they would bring multiple perspectives into consideration to make better choices. Since his election, President Obama has often referred to his reliance on knowing the facts before he makes decisions. While it remains to be seen if an evidential process or blind ideology will prevail in our political system, we’ve seen the power of evidence to break down inertia and enable new ideas to advance. If it plays on Pennsylvania Avenue, why not in Architecture and other design professions?

Design is often cast as an act of intuitive creativity, uniquely owned by the designer and set in a context of ambiguity and uncertainty. Many architects shroud their decisions under a cloak of mystery, inaccessible even to their clients, who are expected to approve their designers’ recommendations through acts of faith. The idea of making transparent the basis on which design decisions are made is unsettling to many designers. They don’t think of evidence as a freeing agent. Instead it’s considered an obstacle to simplifying an essential design parti. With this mindset, rather than “evidence for design,” there is seemingly a choice between “evidence or design.”

Fear of evidence isn’t because designers haven’t used it before. Every design decision, no matter how small or complex, is informed by evidence found in experience, drawn from intuition, or (less often) based on rigorous processes of inquiry. THE CONTINUUM OF EVIDENCE, WEAK AND STRONG, SURROUNDS US. Architects are used to materials performance specifications, codes that were developed based on testing and performance history, and equally comfortable drawing upon their knowledge of their own previous work.

The issue is that most architects don’t think of the current design process as being evidential, whether it is or not. Yes, they use technical data and reflect on other projects; but they feel in control of the process. When that sense of control is lost, such as when a program is very complex and constraining, or a client doesn’t accept the designer’s preferred concept, the work seems less creative and personally satisfying. Even more troublesome, when the evidence comes from disciplines beyond architecture, it might be fascinating, but there’s no clear way to directly apply it. Once again, a choice is set up between evidence and design.

SIMPLY PUT, MANY DESIGNERS FEEL THAT THE NOTION OF “EVIDENCE” IS FOREIGN TO THE DESIGN PROCESS THEY KNOW. Over several years of looking at attitudes about evidence-based design (EBD), the authors have found a number of consistent concerns (and myths).

• EBD is too scientific. Creativity is not all about facts. The process of creating is subjective and inductive. It starts with a spark of inspiration. Science is deductive and all rational.

• EBD is reminiscent of a legal process. There are rules about how to consider evidence and decisions must follow the rules. It’s about right and wrong. Personal judgment is diminished.

• EBD is prescriptive. It limits options and stifles innovation.

• EBD is too expensive and time-consuming for most practitioners.

• EBD requires sharing of knowledge that is better kept proprietary for marketing purposes.

• EBD uses facts to evaluate design success. This exposes our work to criticism and could harm our relationships with our clients or even expose us to legal problems.

To what extent are these concerns based in truth? Are there benefits to EBD that make it worthwhile, even if it demands a new mindset? Is there a model for evidence-based practice that is specifically right for the design professions?

Not As It Seems

The first step in getting past the myths and fears is deciding if EBD would be of value. Is there even a good reason to rethink how we design? THE VALUE OF EBD CAN ONLY BE UNDERSTOOD IN THE CONTEXT OF THE VALUE OF DESIGN AS A CONTRIBUTOR TO SOCIETY.

Architectural form that delights has great value. But more is possible. The public may be enamored by a structural tour de force or a landmark design that captures their spirit, but when they put on their client hat, they know they are responsible for delivering value to their organization or institution. Rarely will a new design for a hospital, school, or office building be judged by the client on the basis of aesthetics alone. The value of the facility will be attributed to how well it helps attract and motivate talent, support the needs of customers, and achieve financial targets. Will the design foster healing, learning, collaboration, creativity, productivity, or profitability? Will performance outcomes be enhanced by the design or is it merely a beautiful expense? WITH THE RIGHT EVIDENCE, DESIGN CAN DELIGHT AND SERVE.

Intuitively, many designers and their clients sense that students learn better, patients heal faster, and office workers produce more in certain types of environments; or, in fact, that the physical environment can influence human well-being and performance. There is mounting evidence that we can influence organizational performance through design but rarely is evidence used to guarantee those outcomes. Why?

In part, we don’t have access to credible, applicable evidence, or we aren’t aware of how to find it. Most of us aren’t educated as researchers and can’t tell whether what we read is good evidence or misinformation. (There’s no TRANSPARENCY about how it was developed and the qualifications of the person who developed it.) Research takes time. If a client doesn’t demand it, why do it? Then there are the fears: loss of control and creativity.

THINGS ARE NOT AS THEY SEEM, IF ONE THINKS THAT THE PROFESSION CAN CONTINUE AS IT IS WITHOUT CHANGE. Clients do expect more than traditional form-making. They are accountable to their organizations to provide more. Designers who offer more are hired; those who deliver more are hired more than once. Designers who don’t accept their clients’ mandate to deliver functional and financial value have been finding their roles diminished.

We must reevaluate how we design and change the tide of how we are perceived. If we can back up our design recommendations with credible evidence, our judgment will appear more dependable and our recommendations will more likely produce the results we’ve promised. Whether that’s merely fulfilling our professional obligations or enhancing our relationships and quality of work is an interesting argument. However we view it, for the design professions to remain viable, the use of evidence that will help us satisfy our clients’ needs on their terms and create places that really work well for people is inevitable.

Things are also not as they seem in terms of the fears we discussed previously. Creativity does exist in science. Intuition plays an important role. Professional judgment will always be needed. Our past proves that we can incorporate evidence without the design process becoming prescriptive. That’s because evidence is not prescriptive. Just as in the legal context, evidence only informs judgment, making it better. Anyone who has served on a jury knows that deliberations are anything but black and white. Lastly, sharing knowledge and learning by doing are ways we can access more evidence, which is indeed sometimes hard to find without great time and effort.

Time for a Makeover

In recent years, a number of design professionals have embraced the notion of evidence-based design practice, as a model for rigorously seeking or conducting research to predict how well specific design proposals will support desired performance outcomes or conversely, inadvertently cause harm. We’ve tried to learn from similar movements in other professions (i.e., medicine, education, engineering) and we’ve questioned the relevance of lessons from those fields to the architectural profession. We’ve challenged both the quality of nonscientific evidence and the applicability of scientific method.

THE HEALTH OF OUR PROFESSION, MEASURED BY THE PERCEIVED AND DELIVERED VALUE OF OUR SERVICES, DEPENDS ON OUR EMBRACING OUR CLIENTS’ MANDATES TO PROVIDE PHYSICAL ENVIRONMENTS THAT SUPPORT ORGANIZATIONAL PERFORMANCE OBJECTIVES. IN THIS WORLD, THE IMPACTS OF DESIGN ON THE PEOPLE WHO USE THE ENVIRONMENTS MUST BE ANTICIPATED AND RESOLUTIONS PROPOSED THAT INCLUDE VALIDATION THAT DESIGN WILL FACILITATE PROMISED OUTCOMES. EBD CAN DO THIS.

Many proponents of evidenced-based practice agree that we need to look beyond our individual practices and share what we learn across the profession, just as we have traditionally worked together to create and document technical data in codes and standards that provide performance standards for determining appropriate action. Much can be learned from program analysis, client web surveys, and other techniques that are project-specific based. But evidence-based practice must ground itself in broader, deeper data, feasible only from a system that enables us to draw evidence from sources beyond the individual project.

PREVAILING LACK OF KNOWLEDGE OF RESEARCH METHODS IS ANOTHER HURDLE TO JUMP. Few design professionals are trained as researchers or even sensitized to critically evaluate research quality. Our academic settings and professional practice rarely place value on rigorous methodologies for creating and interpreting the information used to inform design. Even the basic steps of scientific method—define the problem, create a hypothesis, test, and document—are seldom followed by designers. Hopefully, the profession will make clear to our educational system that we demand some level of research knowledge as part of basic design education, because it will be as important to our professional success as design and technical capability. In the interim, we can share experience to establish a baseline of professional credibility in EBD.

All of this dialogue (with and without agreement) makes this an exciting time to develop a forward-thinking approach for evidence-based practice, including creating the infrastructure to produce and archive evidence. The profession has progressed to a point where there is interest and awareness of its potential, despite the hesitancy as to how these future opportunities will influence practice. There have been some successes that have established a foundation for additional research. AHEAD OF US AS A PROFESSION IS THE NEED TO ESTABLISH A SET OF STANDARDS AND GUIDELINES TO ASSURE HIGH-QUALITY EVIDENCE AND AN EFFECTIVE SYSTEM FOR CREATING, ARCHIVING, AND DISSEMINATING THIS EVIDENCE.

It is our judgment that this will be the next major transformation of our profession. It will create a context for significant multidisciplinary dialogue and collaborative work between the academies and the profession. These opportunities will excite the research-oriented professionals more than others but we will share the benefits of transformation. But it is a time for our total profession to engage its future.

What Will It Look Like?

William Sackett, a proponent of evidence-based medicine, identifies a core principle of evidence-based medicine as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients.” The practice of evidence-based medicine, according to Sackett, “integrates individual clinical experience with the best available external clinical evidence from systematic research.”

A similar definition of EBD has been proposed by Kirk Hamilton. “Evidence-Based Design is the process of basing decisions about the built environment on credible research to achieve the best possible outcomes.” (The Center for Health Design.)

Conceptually, EBD advocates a balanced integration of the skills and experience of the design practitioner, the client’s needs, and critically assessed evidence of various types. THESE INCLUDE EVIDENCE GROUNDED IN RIGOROUS SCIENTIFIC METHODOLOGY AS WELL AS A CONTINUUM OF LEVELS OF EVIDENCE INCLUDING PERSONAL EXPERIENCE AND INTUITION. Art and science of designing are both respected but interpreted each according to its strengths and weaknesses as predictors of design impacts on human outcomes. Both are central to making these outcomes transparent to all of the stakeholders.

Perhaps the most important lesson for EBD from evidence-based medicine is the notion of “strength of evidence.” By definition, architects integrate various types of evidence—cultural, technical, and artistic. In the practice of architecture, it’s unlikely that the act of designing would deliberately avoid valuable inputs, or at least so it would seem. To the contrary, architects and other designers frequently claim to care about the outcomes of their work. They say they create hospitals that heal, schools that help students learn, and offices that enhance productivity.

Standards vary across and within disciplines. Some give credibility to multiple types of evidence, developed with various methods. Others show a bias for rigorous, systematic, and objective methods, such as randomized experiments—the “gold standard” of scientific research.

Lastly, the practice of EBD must be based on transparency. Design practices often try to use what we learn for competitive advantage. They are reluctant to be scrutinized. In contrast, the culture of peer review that science embraces ensures quality improvement. Self-conducted research is sometimes publicized but the methods by which data was collected, variables controlled, results analyzed, and findings interpreted are seldom fully revealed.

Without transparent, clear, and complete communication, it’s inevitable that findings will be taken out of context, misapplied, and overgeneralized. All of these ultimately serve clients badly and discredit the process of using evidence. Designers don’t have to become researchers but they do need to understand the basics of how to use research correctly. With greater transparency, practitioners will have greater access to useful knowledge and the ability to effectively judge if the knowledge applies to their project.

The Authors’ Journey

We set out on a journey to explore our theories about evidence-based design in the context of work being done by leading experts. We started with some assumptions and questions:

• Many research methodologies have merit in their respective disciplines. Our assumption is that design can be informed by many of these. The work discussed in the following chapters explores how different methodologies can be used effectively to address different types of design questions. We need to go beyond only postoccupancy evaluations of our own projects to achieve the richness of the examples covered in this book.

• Are some methodologies better suited to most questions that typical design practitioners confront? Some research requires depth of knowledge, time, and funding that most projects can’t sustain. The work in the following chapters makes evident that we can develop some useful evidence during projects that will be sufficient to spark an innovation or provide assurance that client needs are being addressed. Other research, also of value, will have to be conducted outside of project timelines and fees but can then be applied to great benefit.

• We’ve observed a disconnect between some research and the evidence designers need. We’ve presented examples of architectural research that was focused on application and also were reliable in predicting how specific design elements might impact outcomes.

• There is a need to expand beyond our associations with architects, engineers, and contractors to interdisciplinary collaborations that would bring new research methods to the production and translation of evidence for application in design. The work in the following chapters illustrates the benefits of collaboration across disciplines.

• Our premise is that it’s fallacious to depend upon intuition and experience alone. Doing so does not serve our profession well, especially because few architects obtain systematic feedback on actual performance outcomes. There is limited systematic knowledge transfer from project to project or from project to generalized knowledge into the intellectual capital of the profession. We’ve sought and found examples of other ways to develop the knowledge base.

• Once validated evidence is developed, there needs to be an organizational and systematic infrastructure created to store, archive, and provide open-access to individuals, firms, and the profession as a whole. This issue is discussed by experts in the interviews that follow.

• There is a need to establish clear and accepted standards and guidelines for what constitutes “credible evidence,” how it is nested in terms of other related evidence, and what are the methods and processes for its creation and application.

The work we found on our journey loosely falls into three large categories as a framework for discussion. The focus here is how these three categories of evidence could enhance building performance and human experience, as well as enrich the formal process of designing and making physical environments. Some subcomponents of each of these categories have evidence that is robust and is already used regularly. Others are quite new and are only beginning to have an impact on design decision-making as guarantors of performance outcomes. Still others are only in the incubator stage but show great promise.

1. MODELING, SIMULATION, AND DATA MINING

Modeling is a set of activities that structures innovation, collaboration, and creativity in design by creating physical and virtual models of objects under investigation by designers. This activity is guided by a hypothesis or question that enables the designer to test components or systems as a thinking-by-doing activity. It is an iterative process that provides the framework for testing performance of materials, construction strategies, and other physical phenomena at various scales including scaled models to full-size representations. This process is sometimes referred to as reflective practice—the working through of a design question by making artifacts that represent the intended outcomes, rather than just thinking about the challenge. This approach points out that physical action and cognition are interconnected. Successful designs result from a series of conversations with various phenomena, with the conversation being between the designer and the medium of design, virtual drawings and models, paper, clay, chipboard, and real materials, constantly making and testing the outcomes to observe performance indicators.

Simulation is a process for understanding the interactions of the parts of a system and the system as a whole. A system is an entity, which maintains its existence through the interactions of its parts or components. A model is a simplified representation of the actual system to promote the understanding of the performance of the parts in the context of the whole system. Since all models are simplifications of reality, there is always a trade-off as to what level of detail is included in the model. Too little detail risks missing relevant interactions. Too much detail may overcomplicate the model, making it difficult to understand the nature of the interactions. Simulation is generally referred to as a computational version of a model. Simulations are generally iterative. One develops a model, simulates it, learns from the simulation, revisits the model, and resimulates the condition until an adequate level of understanding of the relationships of the parts to the whole is reached.

Critical to both of these technologies is the willingness of the designer to create a hypothesis about an artifact to be tested, either physically or virtually, knowing the perception of the artifact is incomplete or maybe even wrong. The iterative process of testing and evaluation, modeling, or simulating transforms an artifact toward some specific articulated performance outcome, use/ activity, light, behavior, etc. This process is not about rationalizing an idea or a vision, but one of transformation of ideas and visions to meet specific performance outcomes. The more rigorous and transparent the performance outcomes, the more transparent the design process will become connecting the artifact to evidence that supports increases in performance.

Data mining is the extraction of hidden relationships from large databases. This is a powerful new technology with great potential in helping organizations focus on the most important information in their data warehouses—their organization’s intellectual capital. The literature refers to this process as “super crunching.” The data used to capture and record these found relationships comes from many diverse sources (i.e., personal experience, completed projects records and documentation, and other artifacts developed to support organizational activity). This process of data crunching can suggest predictive future trends and behaviors, and the identification of important questions or hypotheses about future activities and directions within a project or organizational setting. This process of mining data allows organizations and individuals to make proactive and evidence-based, knowledge-driven decisions rooted on these predictive futures. Designers and their associated professions—engineering, construction, planning, and other design entities—have large sets of data, usually project-specific, but what is missing is the infrastructure to access and utilize this data in a longitudinal sense. Data mining and its associated tools provide a platform for creating this new infrastructure with the capacity to capture, share, understand, and utilize the existing data across and within our professions.

2. SOCIAL SCIENCES

The use of evidence gained through study of the social sciences provides understanding of human behavior through scientific explanation. The process explores human desires, preferences, attitudes, perceptions, and motivations. Of most relevance to design are sociology and psychology. The discipline of environmental psychology specifically addresses the convergence of the two fields but other social science research, such as developmental and cognitive psychology, can enrich a designer’s understanding of the behaviors to be supported—or transformed—by the environment. Social science contributes to informed design by providing methodologies with which place-behavior relationships can be studied, as well as knowledge about why people behave as they do and why they might respond to physical surroundings in predictable ways.

3. THE PHYSICAL AND NATURAL SCIENCES

Within the sciences, the physical and natural sciences are often seen as a single category that is contrasted to the social sciences. While the physical and natural are considered to be “hard sciences,” using similar research bases and methodologies, they are in fact two very distinct sciences when related to architecture. The physical sciences, especially physics, have a long history that provides a foundation for architectural design of structures, mechanical and electrical systems, and the process of “making.” Today, the physical sciences continue to provide a rich area of research in issues related to building performance.

In many instances, the incentive for this research is being driven by a global commitment to the design of more sustainable buildings that reflect energy-conserving approaches to design, improved building systems, and creation of new and innovative sustainable materials. On a global scale, research and advances in building sciences are being driven by a need for more advanced technologies to build taller structures with new systems and materials or new environmental technologies to respond to harsh geographic conditions. Sources for both of these advances come from research groups related to academic institutions, private industry manufacturers, and architectural practitioners seeking more responsive design approaches.

The natural sciences, with a foundation in biology, have had less of a direct impact on architecture than have the physical sciences. However, that’s about to change! Within the past 25 to 30 years, the field of Neuroscience has blossomed and has stimulated an interest by neuroscientists in understanding how, why, and what parts of the brain respond to environmental stimuli and experiences, including light, sound, scale, proportion, perspective, and the like—all tools used by architects in design.

The focus of neuroscience and the cognitive sciences has unlocked a new domain for understanding human performance in physical settings. The research that is emerging from the disciplines of neuroscience and the cognitive sciences is providing new directions for developing and utilizing research evidence for use in an evidence-based design practice. By capturing the mental processes from scans of the brain, as a person moves, sees, hears, and experiences motion in space, one can correlate physiological measures with issues of stress, satisfaction, and emotion. This understanding can then be used as evidence to predict the impact of physical spatial attributes on human performance. This is a new arena for both the production and utilization of evidence; and it holds great promise for the future of the design professions.

If an understanding and collaboration with physical science represents the sophistication of tools available to us today, then neuroscience represents an equally sophisticated but yet untapped future for collaboration.

The Road Well Taken

THE DIALOGUE THAT WILL CLARIFY A DIRECTION FOR THIS NEW INNOVATIVE AND FORWARD-THINKING FUTURE IS CRITICAL, AS IT WILL DEFINE OUR ROLES AS DESIGN PROFESSIONALS. IT WILL ALSO SET THE AGENDAS FOR OUR ACADEMIES, WITH WHICH THEY WILL ESTABLISH GUIDELINES FOR THE NEXT GENERATION OF EVIDENCE-BASED PROFESSIONALS.

The primary goal of this book is to focus on the future of evidence-based practice and the mechanisms that would produce a new direction for the future of design professionals. The book does not look back at where we have been, but intentionally looks forward to help chart a map that moves into that future. The chapters that follow document the richness of work currently being undertaken by researchers and practitioners in a variety of fields that are establishing that new future.

The people that were selected are individuals who focus on innovation by utilizing twenty-first-century technologies, methods, and disciplinary content to explore this new frontier. The questions they are asking, the methods they are utilizing, and the outcomes they are producing, are making major contributions to redefining the landscape of design, architecture, and construction. In the majority of cases these are people who are working on projects that are organized around interdisciplinary teams or are creating new transdisciplinary organizations. They are applying new computational technologies, scientific discoveries, and organizational agendas to resolve the challenges of the day—sustainability, human performance, environmental degradation, and transportation alternatives; at the same time as they are developing new models of work—innovative research methods, fabrication technologies, and performance-predictive tools.

The next three chapters document the interviews with the selected individuals. Each interview was guided by a set of eight questions. These questions represented only the beginning points of the dynamic process of engaging each of the interviewees in a dialogue about their work and how evidence is a major component of their process of working on critical design issues. These base questions helped facilitate further discussion about the interviewees’ contributions to specific projects and to an infrastructure for producing evidence. The base questions listed below were only launching points and the detailed questions in each interview were a means to bore deeper into the interviewees’ work and process.

• How do you use research in your work and how does it inform design?

• How is evidence produced and how does evidence influence your work?

• What are the core methods, skills, and values needed to do evidence-based design or to produce evidence in your practice or institutional setting?

• Does the use of evidence inhibit or enhance the nature of your work?

• How does interdisciplinary collaboration play a part in your work?

• How much evidence is enough and what makes it credible?

• How are the outcomes of your work translated so that they can be generalized and used by others?

• From your perspective what should be the future models of education and practice to support an evidence-based practice?

In some cases the interviews are followed by case studies that address four important questions:

• What was the research question?

• What was the research method?

• What were the research outcomes?

• How did they inform design?

Following the three chapters about the three categories of evidence-based design are two major case studies. One is of the new California Academy of Science and the second is a summary of the outcomes from the 2005 College of Fellows of American Institute of Architects Latrobe Fellowship. The first case study documents an important new building where formal visions and ideas are supported, refined, and strengthened by bringing evidence from other disciplines to increase building and human performance, and enhance the human experience through architecture.

The second case study, The 2005 Latrobe Fellowship, was awarded to Chong Partners Architecture (now Stantec Architecture), Kaiser Permanente, and the University of California, Berkeley, to explore the use of evidence from the social and physiological sciences to better understand human response to design, and thereby make better design decisions.

The research focused specifically on health-care environments and the question of whether design can aid healing. (Reduced “time to heal” and medical errors, in turn, increase hospital financial performance.) Credible evidence of design impacts on healing and errors does exist; but so do assertions based on very weak indicators. The Latrobe research sought a model for creating evidence that would be reliably strong. Chapter 6 summarizes the processes and outcomes of that research effort conducted in a collaborative format.

IT IS CLEAR THAT INNOVATION IS ALL AROUND US. EXTRAORDINARY EFFORTS TO BETTER DESCRIBE AND APPLY EVIDENCE ABOUT THE RELATIONSHIPS BETWEEN DESIGN AND HUMAN PERFORMANCE ARE BEING UNDERTAKEN BY PEOPLE FROM VARIOUS DISCIPLINES AND WITH DIFFERING PERSPECTIVES AS ILLUSTRATED IN THE INTERVIEWS. THESE EFFORTS WILL MOVE FORWARD THE ACCEPTANCE OF EBD AS A PROCESS; WILL ENLARGE THE BODY OF KNOWLEDGE AVAILABLE TO THE PROFESSION; AND WILL IMPROVE OUR ABILITY TO ANTICIPATE, IF NOT PREDICT, WAYS TO USE DESIGN TO ENRICH LIVES.

2

Models, Simulation, and Data Mining

Background and Context

Introduction

PHYSICAL MODELS, PROTOTYPES, AND TESTING HAVE LONG TRADITIONS IN THE DESIGN PROFESSIONS. They have been pivotal activities in innovation, collaboration, and creativity when creating design outcomes, and testing performance capacities. Our first evidence of this type of formal design activity was in the PHYSICAL MODELS BUILT AT FULL SCALE AND TESTED TO FAILURE—A PROCESS OF TRIAL AND ERROR UNTIL FAILURE DID NOT RESULT. The great pyramids, the Roman coliseums, temples, as well as many of the wonderful Gothic cathedrals were designed and built this way.

Overview of Models, Simulation, and Data Mining

Over the past centuries, this process of designing changed based on advances in technology, new building materials and construction methods, and major changes in how designers organized their work processes. The introduction of new tools, such as parametric drawing and perspective and physical scaled models, influenced these changes. In recent years, however, the use of physical models and prototypes has taken on new meaning and now represents a source of many research investigations that are either project-specific or focused on the larger agendas of understanding either subsystems or whole building performance. Historically, modeling and prototyping has been carried out using physical artifacts that could be tested by external processes, stress, heat transfer, lighting levels, etc. However, in recent years innovations in digital technologies have transformed this process. Now digital simulations are the primary medium for representing and testing design intent and design performance expectations.

SIMULATION IS A TWENTY-FIRST-CENTURY TECHNOLOGY FOR UNDERSTANDING THE INTERACTIONS OF THE PARTS OF A SYSTEM, AS WELL AS THE SYSTEM AS A WHOLE, THROUGH DIGITAL REPRESENTATIONS OF THE DESIGN ELEMENTS IN QUESTION AND THE RESULTING PERFORMANCE UNDER SPECIFIC TESTING SCENARIOS. Simulation technology is a computational version of a model that has been created to study the implications of the defined interactions over time and in various contextual conditions. Simulations are generally iterative. First, a computational model is represented in an appropriate form—2D or 3D. Then, a model is run with simulated conditions that emulate real life forces to which the actual building might be exposed. Performance outcomes are established and evaluated; and the model is revised and other simulations run until an adequate level of performance outcome has been reached, based on the understanding of the relationships of the parts to the whole of the system under consideration.

In the past, it was believed that one could get by with intuition and experience. The information age we now live in has substantially changed that view. Today the name of the game is “data.” Data-driven decision-making is central not only to our everyday lives, but to the future of most, if not all, professional activities. The architecture, engineering, and construction industries (AEC) are struggling to engage this agenda. Each of these professions has relied heavily on experience as the primary source of knowledge, skills, and values to inform their decision-making process; the exceptions being primarily related to building technology. In some cases, it is apparent that the professionals involved resist acknowledging any value to using data/evidence to inform their design activities, as they feel it gets in the way of their creativity.

In a recent interview with Phil Bernstein, Vice President of Autodesk and a faculty member at Yale University, he noted that “it is clear that the development of new technologies for organizing, representing, and analyzing data will continue to outpace the availability of data.” This presents us with a significant challenge. Our challenge in the AEC industry is not the availability of data, but to have an infrastructure that creates access to data that designers can understand, interpret, and act on to inform design and construction.

The Influences of Physical Models, Prototyping, Testing, and Simulation on Architecture

PHYSICAL MODELS AND PROTOTYPES ARE GROUNDED IN EXPLICIT OR IMPLICIT HYPOTHESES ABOUT DESIGN PERFORMANCE, ENABLING THE DESIGNER TO TEST THE RELATIONSHIPS BETWEEN DESIGN INTENT AND DESIGN-OUTCOME PERFORMANCE. THE PROCESS OF THINKING-BY-DOING THROUGH A SET OF ITERATIONS IS HIGHLY RELEVANT TO CREATING ARCHITECTURE. The design studio, either in practice or in the academy, is the armature for establishing this type of culture for the process of making physical environments. Donald A. Schon (1983) referred to this type of thinking-by-doing as “reflective practice.” This form of practice frames a set of design conditions and evaluates the outcomes through “conversation” between the designer and the artifact that is being created. These informative interchange is facilitated by various media—paper, clay, sketches, physical systems, 2D or 3D simulation, and other representations by stimulating the designer’s thoughts with evidence extracted from the physical models or prototypes.. This evidence can be tacit and personal or have strong analytical foundations through rigorous scientific research. In either case, the physical models and prototypes reveal to the designer potential outcomes that could not have been understood without testing a physical or virtual representation of the built form being considered. Armed with this knowledge, the designer can make better decisions about the design.

THIS PROCESS OF DESIGN AND EVIDENCE APPLICATION REQUIRES ITERATIVE CYCLES OF FRAMING A CHALLENGE—A HYPOTHESIS—AND MEASURING THE PERFORMANCE OUTCOMES RESULTING FROM SPECIFIC DESIGN INTENTIONS AND ACTIONS. The challenge of this approach is that it is expensive and time-consuming to use, build, test, and modify physical models. The digital world has a solution for at least some contexts. Virtual models using simulation technologies are replacing some physical material models in the making of prototypes. The introduction of the digital 3D printer and laser technologies also has changed how physical models provide new and creative interests in prototypes as an outcome of design.

NEW METHODS OF MODELING AND SIMULATION HAVE REVOLUTIONIZED THE AEC PROFESSIONS. This revolution has changed the way we work and even the nature of the work we do. New simulation and modeling tools have changed roles, organizational boundaries, and work processes for architects, engineers and contractors. The infrastructure of the industry itself is shifting. With the adaptation of these new modeling and simulation tools, traditional inefficiencies and adversarial relationships are yielding to a redefined practice model grounded in multidisciplinary collaboration and information sharing among and across project and disciplinary team members. This new framework has been referred to as integrated practice delivery (IPD).

Building information modeling (BIM) is a major element of this IPD practice model. BIM is a parametric, object-based software, which simulates and models a three-dimensional representation of virtual buildings, drawing from complex databases that include information about use, material, structure, energy usage, cost, scheduling, fabrication details, and formal and spatial conditions. At the present time, the technology of BIM is outstripping the availability of quality database information to inform the simulation and modeling outcomes (Bernstein interview, 2009). Much of this challenge is connected to the lack of an infrastructure within the design and construction disciplines and professions for creating, archiving, and sharing data. Put more simply, it is the lack of a research tradition in both the academy and in practice that underpins this challenge. BIM will fall short of its full potential to predict performance outcomes until evidential data becomes readily available to inform the models.

Data mining and its associated tools provide a platform from which to construct a new infrastructure for capturing, sharing, understanding, and using existing data across our professions. Three major constructs must be clearly differentiated to understand the data mining process. Data is defined as any fact, number, illustration, or text that can be represented so it can be processed by computational methods. Information is the patterns, associations, or relationships among data points. Knowledge, on the other hand, is the acknowledgment of patterns and trends in information-insightful data.

Models, Simulation, and Data Mining

Modeling is a simplified representation of the actual system to promote the understanding of the parts to the whole system. A good model depends on how well it represents the relationship and understanding of the parts to the whole and the whole to the parts. Since all models are a SIMPLIFICATION OF REALITY, there is always a trade-off as to what level of detail is included in the model. Too little detail risks missing relevant interactions. Too much detail may overcomplicate the model, making it difficult to understand the nature of the interactions.

Simulation is a computational version of a model; and, like a physical model, it’s a mechanism to represent the implications of the defined interactions under specific conditions. Simulations are generally iterative. One develops a model, simulates it, learns from simulation, revisits the model, and resimulates the condition until an adequate level of understanding of the relationships of the parts to the whole is reached.

Prototyping is an early approximation of system or product that is being designed through testing and being reworked until it reaches desired performance levels; the prototype helps set the standards for the eventual system or object (e.g., element of a building). This process works best when not all of the project requirements are known in detail ahead of time and it is an ITERATIVE PROCESS between the designers and the users and the artifact under consideration.

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help organizations focus on the most important information in their data warehouses—THE ORGANIZATION’S INTELLECTUAL CAPITAL. This includes personal experience data from surveys and evaluations; financial, audit, and compliance data; outcome data, such as energy utilization records, structural loading conditions, employees comfort ratings, and assessments of completed projects; and many of records of organizational activities. Data mining tools can find new relationships between individual data points, establishing predictions of behavioral outcomes, and forecasting trends that can help organizations and individuals make proactive, knowledge-driven decisions. Most organizations and/or professions have massive quantities of data. Architecture and its associated professions—engineering, construction, planning, and other design entities—are no exception in having access to large sets of data. What is missing is the infrastructure to access and use this data.

The concept of data mining is relatively new, but the technology to do it is not. For many years businesses and governments have used computational methods to study volumes of data to reveal trends and relationships. The continual increase in computational power has only accelerated this effort; with two phenomena - both related to computational capacity being key to the increasing use of data mining. The first is Moore’s Law, which notes that computational processing power doubles every two years. The second, Kryder’s law, is the doubling of the storage capacity of hard drives every two years.

The Research Methods of Modeling, Simulation, and Data Mining

WHETHER ONE USES VIRTUAL OR PHYSICAL MODELS, THERE ARE SEVERAL CRITERIA THAT CONTRIBUTE TO MAKING THIS PROCESS BENEFICIAL TO DESIGN AND TO ESTABLISHING A FOUNDATION FOR RESEARCH ACTIVITY. The primary value of these tools is the creation of the databases used to inform the models, simulations, and prototypes. If the modeling process is digital, then a rich database provides the opportunity to analyze design: performance relationships across many projects, thereby enabling inferences about project types. Non-digital (physical) models or prototypes provide similar opportunities to inform and predict, but additional data coding is required so that objective interpretations can be made. In both cases, the designer must make clear the analysis methods used to predict outcome performance.

The assumptions that were used in the model, as well as the methods and standards used to analyze and interpret the data, must be transparent to designers who will use the test results to make design decisions. Without clarity about the process, the designer won’t be able to use professional judgment about the meaning and applicability of the findings. Finally, there must be solid peer acceptance of the analytical methods, so outcomes will have credibility in the larger context of practice.

Physical models and prototypes are used to simulate and analyze material properties, as part of systems development and integration and to detect and resolve conflicts within complex systems. Physical models are typically used to test and calibrate performance metrics, such as pressure flow, stress, strain, vibration, or other forces that influence a system. In most cases today, physical models, virtual simulations, and analysis tools are used simultaneously to explore and understand the performance and to provide data warehouses for data mining research efforts.

Physical Modeling, Prototyping, Testing, and Simulation Research Opportunities for Use, Best Practices, and Best Context

VIRTUAL MODELS AND PROTOTYPES ARE EXCELLENT TOOLS FOR TESTING THE FORM, FIT, AND FUNCTION OF ELEMENT. AT THE SAME TIME, THEY PROVIDE AN IDEAL FRAMEWORK FOR INTEGRATING ELEMENTS AND SUBSYSTEMS TOGETHER AND EVALUATING WHOLE-SYSTEM (AND EVEN MULTI-SYSTEM) PERFORMANCE. Virtual models and prototypes have capacity to optimize and validate the impact of this, integrating many building elements and complex building systems. Nevertheless, in most cases it is still necessary to use non-digital, physical models to explicitly illustrate the performance of real materials and construction processes in a way that’s credible to the design, construction, and client team. The most effective and informed decisions from the evidence developed by both modeling methods.

The relationship of virtual prototypes and physical models have become much more coordinated, due to new digital tools, including 3D printing, rapid prototyping, and fabrication technologies. There are numerous examples of this project design, as well as full-scale fabrication and construction.

The use of these tools allows the designer to answer a set of very important questions: How do the pieces fit together? How will they be used and perform…and does that satisfy project goals? Will the design have the desired aesthetic impact? Will it be cost effective? By providing evidence by which performance can be predicted and making transparent the degree of relevance of that evidence, virtual and physical modeling enable good decisions about complex design issues.

Data mining links transactional and analytical systems. The software analyzes relational patterns in stored transaction data based on open-ended user queries. Several types of software are available: statistical, machine learning, and neural networks. Usually, any one of the four types of relationships are sought (Frand 1996):

• Classes: Searching stored data located in predetermined groups (i.e., medical patients’ personal characteristics)

• Clusters: Data items are grouped according to logical relationships (i.e., consumer satisfaction outcomes)

• Associations: Data identified by associations (i.e., color and human response)

• Sequential patterns: Data that anticipates behavioral patterns or trends (i.e., automobile preference based on income status)

The methods of data mining involve five major components:

• Extracting, transforming, and representing the transactional data.

• Establishing, storing, and managing the data in a multidimensional database system.

• Providing access to analyst specialists and informational technology professionals.

• Analyzing the data by select application software.

• Representing the outcomes—patterns, trends, and relationships—in transparent forms of user applications.

There are several types of analysis that are available to investigate the structures of database data mining. They include:

• Artificial neural networks: Nonlinear predictive models that learn through training and resemble biological neural network structures.

• Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.

• Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.

• Nearest neighbor methods: A technique that classifies each data record in a dataset based on a combination of the classes.

• Rule induction: The extraction of useful “if-then” rules from data based on statistical significance.

• Data visualization: The visual interpretation and representation of complex relationships in multidimensional data records (Frand 2003 UCLA).

In research, data mining tools can resolve specific case questions. The data mining process also creates an archive of the data from each case, resulting in a composite database that can help us understand new performance dimensions, interrelated outcomes and processes, and relationships among outcomes over multiple cases. As the database grows, there is greater validation and reliability of the evidence and therefore higher value design decisions that are made using it.

THE INTERVIEWS AND CASE STUDIES THAT FOLLOW ARE EXAMPLES OF HOW THIS RAPIDLY DEVELOPING SET OF TOOLS FOR SIMULATING, MODELING, AND CREATING PHYSICAL MODELS AND PROTOTYPES ARE RESHAPING THE WAY WE DESIGN, CONSTRUCT, AND EVALUATE PERFORMANCE OVER THE LIFECYCLE OF PROJECTS.