Data-Driven Design and Construction E-Book

Randy Deutsch

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

Seitenzahl: 770

Veröffentlichungsjahr: 2015

Beschreibung

“In this comprehensive book, Professor Randy Deutsch has unlocked and laid bare the twenty-first century codice nascosto of architecture. It is data. Big data. Data as driver. . .This book offers us the chance to become informed and knowledgeable pursuers of data and the opportunities it offers to making architecture a wonderful, useful, and smart art form.”

—From the Foreword by James Timberlake, FAIA

Written for architects, engineers, contractors, owners, and educators, and based on today’s technology and practices, Data-Driven Design and Construction: 25 Strategies for Capturing, Applying and Analyzing Building Data

• addresses how innovative individuals and firms are using data to remain competitive while advancing their practices.
• seeks to address and rectify a gap in our learning, by explaining to architects, engineers, contractors and owners—and students of these fields—how to acquire and use data to make more informed decisions.
• documents how data-driven design is the new frontier of the convergence between BIM and architectural computational analyses and associated tools.
• is a book of adaptable strategies you and your organization can apply today to make the most of the data you have at your fingertips.

Data-Driven Design and Construction was written to help design practitioners and their project teams make better use of BIM, and leverage data throughout the building lifecycle.

Leseprobe

Data-Driven Design and Construction

25 STRATEGIES FOR CAPTURING, ANALYZING, AND APPLYING BUILDING DATA

Randy Deutsch, AIA, LEED AP

This book is printed on acid-free paper.

Copyright © 2015 by Randy Deutsch. 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, (978) 750–8400, 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, (201) 748–6011, fax [phone], or online at www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with the 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 damages arising herefrom.

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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Cover image: Copyright © NBBJ Cover design: Wiley

Library of Congress Cataloging-in-Publication Data:

Deutsch, Randy. Data-driven design and construction : 25 strategies for capturing, analyzing and applying building data / Randy Deutsch. pages cm Includes index. ISBN 978-1-118-89870-3 (hardback); ISBN 978-1-118-89921-2 (ebk.); ISBN 978-1-118-89926-7 (ebk.) 1. Building—Data processing. I. Title. TH437.D48 2015 720.285—dc23 2015021964

To my son, Simeon, who taught me to seek out primary sources in writing and in life.

If we have data, let’s look at data. If all we have are opinions, let’s go with mine.

—Jim Barksdale

Contents

Foreword

Preface

Asking the Right Questions

Innovators and Thought Leaders Leveraging Data throughout the Building Lifecycle

What This Book Will Do for You

Show Me the Data

Notes

Acknowledgments

Introduction: Measuring the Immeasurable, Validating the Ineffable

Not One More Thing

Strategies for Practice

Benefits of Gathering, Analyzing, and Applying Building Data

Challenges of Gathering, Analyzing, and Applying Building Data

Working with Unstructured Data

Working with Data Requires Additional Time and Effort

Notes

Part I: Why Data, Why Now?

Data-Informed Decision-Making

Chapter 1: The Data Turn

Five Factors Leading to the Leveraging of Data and Industry Change

Notes

Chapter 2: A Data-Driven Design Approach for Buildings

Five Trends Leading to the Rise of Data in the AECO Industry

Data-Centric Approaches

Notes

Chapter 3: Learning from Data

Five Factors Ensuring Data Preparedness

Training, Learning, and Working with Data

Notes

Part II: Capturing, Analyzing and Applying Building Data

Where Data Is Found, How and When Data Is Used, and Who Uses It

Chapter 4: Capturing and Mining Project Data

Public Sources of Data

Private Data Sources

Having a Data Collection Strategy

Notes

Chapter 5: Analyzing Data

Analysis versus Analytics

Predictive Analytics

Notes

Chapter 6: Applying Data

First Steps

Data-Enabled Project Teams

Data-Intensive Roles

Leadership in Data

Notes

Part III: What Data Means for You, Your Firm, Profession, and Industry

Responses to the Question “Why” Will Either Convince or They Will Not

Chapter 7: Data in Construction and Operations

Data in Construction

Responding to Change

Linking Design, Construction, and Operations

Standards and Interoperability

Notes

Chapter 8: Data for Building Owners and End Users

Benefits to the Owner

Direction to Work with Data

AECO Firms as Data Intermediaries

Data Visualization Helps Owners Make Decisions

Data-Driven Design Driven by Owners

Notes

Chapter 9: Building a Case for Leveraging Data

Business Intelligence (BI) and Current-State Assessment

Security and Privacy

Sharing Data

Notes

Epilogue: The Future of Data in AEC

Our Data-Driven Future

The Future Is Already Here

Notes

Appendix

Experts, Innovators, and Thought Leaders Interviewed

Organizations and Universities Represented

The 25 Data-Driven Strategies

Software Mentioned

Recommended Reading

Index

EULA

List of Illustrations

Preface

Figure P1: BIM alone won’t improve labor productivity in the AEC industry, which, after 50-plus years of tracking, still lags other nonfarm industries.

Introduction

Figure I.1 A spectrum of decision-making criteria: Data increases credibility.

Figure I.2 Bar-chart city: The importance of starting to see the urban environment in terms, and comprised, of data.

Figure I.3 DIKW progression: Leveraging data to manage complexity.

Figure I.4 Typically illustrated as a pyramid or continuum, DIKW can be thought of as a continuous loop toward increasing certainty.

Figure I.5 Visualization enables clients to walk through iterations of the building as indicated by icons along the bottom of the image: the base case; adding wall insulation; better windows. The less “orange,” the less “blue,” the more white, the better. The bigger the dot, the more energy used.

Figure I.6 Overall View Analysis Diagram.

Figure I.7 The performance wheel is RTKL’s version of the triple bottom line—economic, environmental, and social—that lays out the firm’s design values.

Figure I.8 An early version of the CASE Building Analytics dashboard. The dashboard helps architects and building owners see trends in their projects’ geographic locations, sizes, and program types.

Figure I.9 Beyond BIM, the Dashboard provides a concise interface to compare metrics of a number of SOM projects. The color-coding indicates percentile ranking relative to all SOM buildings of that same type. Metrics include: Net and Gross areas, Building Efficiency, MEP Systems, Glass Types, Lease Span, Elevatoring, and sustainability metrics.

Figure I.10 Hangzhou Stadium. External “Petal” structure: Parametric components of stadium design.

Figure I.11 Sefaira allows architects to compare design options and measure their performance using chosen parameters.

Figure I.12 Building information modeling (BIM) is a tool to document and manage the construction process. But can it be used as a data visualization tool?

Figure I.13 MacLeamy Graph. Patrick MacLeamy advocates for shifting the bulk of design effort earlier in the project to reduce the impact of design changes.

Figure I.14 Using proxy models to satisfy a variety of deliverables with a single data set. Parametric platforms allow users to create multiple versions of a model based upon a shared data set. In doing so, different deliverable requirements are satisfied without the need to manually remodel.

Figure I.15 The Aditazz Realization Platform wheel integrates design, construction, and building of products.

Figure I.16 Data as the ultimate justification for a course of architectural action.

Figure I.17 Cooling tower (Doha, Qatar): Over-clad façade to cooling infrastructure in an urban context. Design data work is extended into construction to automate the manufacture of the formwork shuttering.

Figure I.18 A daily construction photolog and point cloud model generated using the collection of overlapping photos. The elements detected as behind-schedule are color-coded in “red,” and the elements on schedule are color-coded in “green.”

Figure I.19 Organized into three parts, this book provides—in this order—justifications for, explanations for, and descriptions of data use in the AECO industry.

Chapter 1

Figure 1.1 BIM Benchmark measures real-world performance of computer hardware. Users are presented with a series of statistics concerning how quickly their computer executed a series of tasks in a BIM model, allowing them to make more informed hardware-purchasing decisions.

Figure 1.2 A version of the BIM Benchmark tool prototyped at CASE.

Figure 1.3 Shading tests and corresponding changes to cooling loads.

Figure 1.4 Sefaira’s outputs include clear informative graphs that can exported and edited to fit the designer’s brand.

Figure 1.5 Sefaira allows architects to compare design options and measure their performance using chosen parameters. (EUI/ Annual Energy Consumption/ Peak Cooling Demand)

Figure 1.6 Users make comparisons to set the project on the right track early, refine the design as it progresses, and test the effects of design changes (including value engineering).

Figure 1.7 Horizon Cloud. Cloud technology enables a secure pipeline for sharing data across offices and project teams.

Figure 1.8 The Dashboard can be set to flag properties whose values exceed thresholds set by the user. As the Dashboard grows in functionality, roles can be added or modified.

Figure 1.9 Adding a new project involves inputting a number of fields, including market sector, building typology, and status, which can be sourced from an existing database to minimize redundancy and promote data validity.

Figure 1.10 Extended information, such as Contracted Scope and Current Progress, can be added for querying projects at a particular phase.

Figure 1.11 Project uploads, or Harvests, can be checked, tracked, and verified.

Figure 1.12 A Comparison Engine enables a user to check one or more Family Types against Types in another file, such as a Standards file. Results display discrepancies in Families’ Parameter values.

Figure 1.13 Results display discrepancies in Families’ Parameter values for easy management of multi-model projects.

Figure 1.14 Corrections can be made from the Dashboard console and propagated back to their respective models.

Figure 1.15 The BIM Dashboard’s front page gives the user an at-a-glance, high-level understanding of norms for file size, project versions, models by discipline, status of most recent and active projects, and more.

Figure 1.16 Users can drill down and visualize specific anomalies. The graph indicates that the largest project has a third more models than the next largest one, and that the majority of projects have one to six models.

Figure 1.17 The Project page shows information about all of the models that constitute the Federated Model. The file size and aggregate number of warnings for the overall project remain constant—a sign that the project is very well managed.

Figure 1.18 The Project Model Page gives an immediate status of the health of a project based on a number of commonly agreed-upon metrics. The Model’s history is also included, providing additional insight as to future performance.

Figure 1.19 The Warnings functionality logs each warning from a model and remembers elements associated with that warning, allowing the user to track unique warning instances.

Figure 1.20 A text box provides easy copy/paste access to Warning Element IDs so they can quickly be selected in Revit.

Figure 1.21 Any of the Warnings can be expanded, revealing Element IDs that are indicated below. Each grouping indicates a unique Warning Instance.

Figure 1.22 Three basic data types in AEC parametric modeling: inherent geometric data, external generative data, and supplemental BIM data.

Figure 1.23 DIKW Progression. To arrive at relevant and meaningful decisions, data must first pass through the BIM model.

Figure 1.24 Leveraging “big data.” Experiment with how your organization will leverage data to make better decisions, bring about better insights, and make better buildings.

Figure 1.25 AECO industry’s considerable challenges to fully participating in big data.

Figure 1.26 The global overview gives a quick snapshot of key statistics that are monitored daily; here the number of active projects and the activity in the BIM models are displayed.

Figure 1.27 The Building Analytics dashboard provides information on every project the firm has done.

Figure 1.28 Whether geometry, building performance, or human performance, it is all data.

Figure 1.29 Shading analysis using Sefaira’s Daylighting Visualization.

Figure 1.30 Database: Ideas backed up with data is still why many people choose to work with architects.

Figure 1.31 MARS web-based platform for crowd-sourcing construction activity analysis. Users provide annotations on the role, activities, and tools used by the craft workers and the platform extrapolates information to the video frames.

Figure 1.32 Thornton Tomasetti’s CORE studio assisted 360 architects in the panelization of the Rogers Place Arena in Edmonton, Canada. A bottom-up approach was used to derive panel layout controlled by physics engine Kangaroo for Grasshopper.

Figure 1.33 Thornton Tomasetti’s in-house structural design suite: Thornton Tomasetti’s CORE studio developed a number of tools for analysis of complex structures, and data visualization and mining thereof.

Figure 1.34 Thornton Tomasetti’s CORE studio developed an in-house interoperability platform and BIM management suite: TTX.

Figure 1.35 Hurricane Sandy disaster visualization: CORE studio assisted the Property Loss Consulting Group at Thornton Tomasetti in visualizing data captured after investigations.

Figure 1.36 Hurricane Sandy disaster visualization: CORE studio assisted the Property Loss Consulting Group at Thornton Tomasetti in visualizing data captured after investigations.

Figure 1.37 Thornton Tomasetti joint research project with LMN Tech Studio. Remote Solving allows for automated analysis feedback by engineers at concept design phase.

Figure 1.38 Thornton Tomasetti joint research project with LMN Tech Studio. Remote Solving allows for automated analysis feedback by engineers at concept design phase.

Figure 1.39 Thornton Tomasetti in-house structural design suite: Thornton Tomasetti’s CORE studio developed a number of tools for analysis of complex structures, and data visualization and mining thereof.

Chapter 2

Figure 2.1 Data indicates a need for intervention to reduce the time patients wait for staff.

Figure 2.2 Datavized information is searchable anywhere. Each asset has its own page, presenting data that has been extracted from the BIM model, allowing someone on site to access the data without needing to open up a BIM model.

Figure 2.3 LMN developed an energy monitoring system to quantify, record, and visualize the performance improvements of their office renovation.

Figure 2.4 The Aditazz Way: An overview of how the software platform is set to revolutionize building design.

Figure 2.5 Time savings brought about by utilizing Aditazz’s catalog of building products

Figure 2.6 BIM alone won’t improve labor productivity in the AEC industry, which, after more than 50 years of tracking, still lags other nonfarm industries.

Figure 2.7 Step 1: Assessment and Formulation and Step 2: Data Gathering and Analysis through Step 5: Recommendations for Improvement.

Figure 2.8 Impact of adding more exam rooms on number of patients seen per day.

Figure 2.9 Exam Room States. Data visualization indicating the number of exam rooms that are idle, that are used, and those that are wasted.

Figure 2.10 Data Scenarios. Two different means for visualizing data on exam-room wait times.

Figure 2.11 BIM, MIB, and IMB approaches.

Figure 2.12 Variations on BIM approaches using data.

Figure 2.13 BIM. Where is your emphasis? On the building, information (data), or model?

Figure 2.14 Data-driven design: The human/machine data spectrum.

Figure 2.15 To become data-centric, the core of your efforts ought to be focused on firm culture, not technology.

Figure 2.16 External “Petal” structure: Finite analysis model of structure.

Figure 2.17 External “Petal” structure: Grasshopper definition of structural skin system.

Figure 2.18 External “Petal” structure: 3D print of concept design and the parametric model that generated it.

Figure 2.19 External “Petal” structure: Hangzhou Sports Park rendering.

Figure 2.20 External “Petal” structure: Section through final stadium design.

Figure 2.21 External “Petal” structure: Successive geometric dependencies building up detail and complexity.

Figure 2.22 External “Petal” structure: Geometric variations altering petal and truss count.

Figure 2.23 External “Petal” structure: Hangzhou Sports Park aerial rendering.

Figure 2.24 Data-driven design requires whole-brain thinking.

Figure 2.25 We need to do a better job of balancing our tools with our processes.

Figure 2.26 Web application used by Allies and Morrison to manage the internal team distribution throughout the practice’s studios and floors. Linking data to project resourcing, IT equipment, and staff profiles allows management oversight of many metrics in a simple tool also used by staff to find colleagues.

Figure 2.27 Analysis of a typical day of who in the studio was searching for who. Indication of frequency by line width and directionality by arrow.

Figure 2.28 Distribution of submitted total working hours per week over a five-year period.

Figure 2.29 Return on capital: Interactive analysis of time worked and overtime distributed by date by architects. Presented information can be filtered and cross-correlated by individuals, sector, individual project, and director in charge.

Figure 2.30 Charles Street Car Park, Sheffield, UK: Over-clad facade to a new car park composed of a single angled module with "random" distribution. Work was made both to make the population of the pattern random, and then to correct it to

appear

more random.

Figure 2.31 Charles Street Car Park, Sheffield, UK.

Figure 2.32 Charles Street Car Park, Sheffield, UK.

Figure 2.33 Charles Street Car Park, Sheffield, UK.

Figure 2.34 King’s Cross Central Master Plan regenerates 67 acres of central London from a former railyard into a mixed-use development covering residential, commercial, cultural, and retail use connected by a robust framework of streets and spaces. Automated output of an analytical tool (not shown) measures the proportion of an urban condition achieving a benchmark degree of visible sky.

Chapter 3

Figure 3.1 Investment in multidisciplinary project teams and new graduates with emergent technological specializations will be key in managing this change. This diagram depicts working side by side (S x S) to collaboratively develop how algorithms are going to work.

Figure 3.2 By analyzing BIM models associated with the project, project overview reveals current status of the project, highlighting outstanding problems within the model while providing data on recent activity within the model.

Figure 3.3 Proof of concept for direct model-to-fabrication using BIM data. DynaRobo visual programming environment for Revit Dynamo and robotics. Pictured (left to right): Brian Ringley, Colin McCrone, Ian Keough.

Figure 3.4 The Node-ification of Everything: Visual programming has become

de rigueur

for designers interested in leveraging computation in their modeling processes.

Figure 3.5 Using proxy models to satisfy a variety of deliverables with a single dataset. Parametric platforms allow users to create multiple versions of a model based upon a shared dataset.

Figure 3.6 Sheet layouts for fabrication can be derived from the same dataset using proxy models within a parametric definition, making this an effective strategy for a virtual design and construction (VDC) workflow.

Figure 3.7 Arduino microprocessor. Maker Faire Rome 2013.

Figure 3.8 Remapping data allows for any dataset to be proportionally scaled within a numerical range for a given geometric transformation.

Figure 3.9 Challenges of interoperability are not purely concerned with geometric fidelity from one platform to another. The model on the right is a direct reference of the one on the left, each existing concurrently in separate platforms.

Figure 3.10 King’s Cross Central Regeneration master plan: Parametric analysis to optimize for a retail subdivision, floor-to-floor heights, and main entry points.

Figure 3.11 Hangzhou: Geometric construction of stadium risers and external “Petal” structure.

Figure 3.12 Marco Hemmerling’s former student Jens Böke based his final university project on a data-driven process, investigating the movement of students on campus to define the best location for the design of summer pavilion SunSys.

Figure 3.13 The structure itself reacts to the solar radiation so that the orientation of the building follows the sun path, which was taken from the specific weather data.

Figure 3.14 SunSys Pavilion was driven by a computational design approach aiming at an early integration of relevant data to build up a robust and flexible design model.

Figure 3.15 The SunSys Pavilion project’s sun gradients.

Chapter 4

Figure 4.1: The 80/20 rule of generating solutions from captured data.

© R Deutsch

Figure 4.2: Interior rendering: Computational fluid dynamics analysis of building section.

© NBBJ

Figure 4.3: Computational fluid dynamics analysis of building plan.

© NBBJ

Figure 4.4: Google Bay View campus in Mountain View, California.

© NBBJ

Figure 4.5: Koo Foundation exterior rendering.

© NBBJ

Figure 4.6: Samsung: Samsung courtyard rendering.

© NBBJ

Figure 4.7: Samsung: Travel distance + calories.

© NBBJ

Figure 4.8: Samsung: Travel distance + calories.

© NBBJ

Figure 4.9: Samsung: Screenshot of Agent-based model analyzing calorie expenditure, distance traveled, and cross-floor visibility.

© NBBJ

Figure 4.10: Collecting data is just the first step in how data leads to action and how decisions are derived from data.

© R Deutsch

Figure 4.11: Arduino Starter Kit in Italian.

© Arduino LLC

Figure 4.12: (a)

Arduino e la luce

. Sensors are simple little things that measure and report on change, and in so doing they emulate the five human senses: (b) Arduino microprocessor. Sensors are attached to all sorts of living and inert objects so they can share what they observe. (c) Arduino microprocessor with cover (Maker Faire Rome 2013). Sensors work tirelessly, never needing sleep and never demanding a raise. They notice changes where humans miss them. (d) Arduino Robot unboxed. Sensors already know what building you are in. Not too far into the future, your mobile device will also know what floor you are on, what room you are in, and in which direction you are moving. (Robert Scoble and Shel Israel,

Age of Context: Mobile, Sensors, Data and the Future of Privacy

. Patrick Brewster Press, 2014. )

© Arduino LLC

Figure 4.13: Arduino microprocessor. Arduino Meets Wearables Workshop.

© Arduino LLC

Figure 4.14: Data-driven design and construction rely on the capture of reliable data from a variety of sources.

© R Deutsch

Figure 4.15: LMN's use of technology affords a highly iterative design process informed by simulation and analysis of critical project parameters.

© LMN Architects

Figure 4.16: Visualizations of design iterations are named according to the controlling parameters, which allows for later regeneration of a particular iteration.

© LMN Architects

Figure 4.17: A matrix of iterations compares the effectiveness of increasing the glazing percentage to increase daylight coverage.

© LMN Architects

Figure 4.18: Example of a typical shoebox daylight study comparing percent glazing to percent of year that desired lighting levels are achieved.

© LMN Architects

Figure 4.19: LMN is using parametric modeling and iterative simulations to compare bridge alignments and structural configurations in an effort to limit cost and maximize design potential.

© LMN Architects

Figure 4.20: The form and patterning of the University of Iowa School of Music acoustic reflector was iteratively developed based on the acoustical requirements, location of audiovisual equipment, theatrical lighting, and fabrication constraints.

© LMN Architects

Figure 4.21: Through data analysis, the Model Overview can track the objects within a model and how they change over time, giving managers insight into how the model is progressing and where problematic areas may lie.

© CASE

Figure 4.22: Office data workflow.

© Solomon Cordwell Buenz

Figure 4.23: Koo Foundation.

© NBBJ

Figure 4.24: Interior rendering: Koo Foundation interior kitchenette rendering.

© NBBJ

Figure 4.25: Interior rendering: Koo Foundation auditorium rendering.

© NBBJ

Figure 4.26: Sasaki's integrated approach relies on the interaction of many hands.

© Sasaki Associates

Figure 4.27: Network diagram from Brown University shows faculty interaction patterns. The nodes are faculty members, the colors are departments. Nodes that are close together want to collaborate; nodes that are further apart less so.

© Sasaki Associates + Brown University

Figure 4.28: Bad data is worse than no data. Formulae are bad. Emphasize analysis over data.

© R Deutsch

Figure 4.29: The vision of automated video-based assessment on construction sites. By detecting, tracking, and analyzing jobsite activities of equipment and workers in real time, performance metrics can be automatically assessed.

© Mani Golparvar-Fard, Ph.D.

Chapter 5

Figure 5.1 A “strategies and bundles” framework helps identify design strategies with the biggest impact on performance, and find the combinations (“bundles”) that deliver breakthrough performance.

Figure 5.2 Daylight factor visualization in Sefaira’s plug-in.

Figure 5.3 Flowchart showing the incremental impact of environmental strategies on a building.

Figure 5.4 Architects can compare design options and measure their performance using chosen parameters.

Figure 5.5 Architects can toggle between two daylight metrics: Spatial Daylight Autonomy and Daylight Factor.

Figure 5.6 The Sefaira for SketchUp plug-in communicates a building’s performance in an intuitive, easy-to-understand way, showing a breakdown of factors actively affecting the design’s performance.

Figure 5.7 Sefaira for SketchUp plug-in Bad Performance result.

Figure 5.8 Strategies to investigate for holistically optimizing the built environment.

Figure 5.9 A snapshot of Oakland, California’s climate using typical meteorological year (TMY) data to make clear to the client what Loisos + Ubbelohde is doing when using the climate file as an input to energy simulation.

Figure 5.10 A graphic technique created by Loisos + Ubbelohde for a dormitory in Berkeley, California, for electrical lighting design and daylighting. Image shows a value engineering proposition.

Figure 5.11 A visualization looking at the potential for building integrated photovoltaic (BIPV) systems that would shade the building as well as generate energy. The visualization asks: What would be a good angle for the PV versus investing in dynamic PVs?

Figure 5.12 The image on the left is a photograph using high dynamic range (HDR) photography. The image on the right is a simulation using software for predicting daylight performance. The impressive thing about them is that they correlate so well.

Figure 5.13 A description of energy generation versus consumption. Below the middle line plots how much energy will be used for various uses versus how much energy can be generated using PV panels.

Figure 5.14 Natural ventilation diagram from a class called “Building Performance and Visualization” in which students were encouraged to use Dhour to discover how the performance of a case-study building might be improved.

Chapter 6

Figure 6.1 Application of data is action-oriented, arriving after data has been identified, mined, analyzed, and visualized.

Figure 6.2 Options explored using Tally with results per life cycle stage itemized by CSI division.

Figure 6.3 Wireless sensor network.

Figure 6.4 Wireless sensor network.

Figure 6.5 Green roof vegetation study.

Figure 6.6 Green roof vegetation study.

Figure 6.7 Information intermediaries act like digital middlemen between project developers and owners and operators.

Figure 6.8 Information intermediaries serve as digital middlemen integrating and linking data throughout the project life cycle.

Figure 6.9 Architects today increasingly work alongside specialists: hackers, data scientists, and algorithm builders.

Figure 6.10 Data-informed architects think like hackers, data scientists, and algorithm builders.

Figure 6.11 Seeking the intersection of geometry, building performance, and human performance.

Figure 6.12 Design serves as a filter enabling you to think in terms of others.

Figure 6.13 Conceptual structure of the artificial neural network that predicts heating energy consumption.

Figure 6.14 Early design stage energy performance app for schools in England.

Figure 6.15 Who will define and hold the team together?

Chapter 7

Figure 7.1 When you look at data in design, construction, and operations, the design and operations phases form bookends, each making ample use of available data.

Figure 7.2 Construction historically relies on previous experience and practices over and above reliable data.

Figure 7.3 Visualization of construction progress deviations: BIM elements superimposed over a time-lapse image, color-coded based on their progress deviations. Elements behind schedule are color-coded in red, on schedule colored in green.

Figure 7.4 If you’re ahead of schedule, in a coordination meeting this image shows elements in green; if you’re behind, it shows in red.

Figure 7.5 A daily construction photolog from a typical building construction site. On average, about 200250 photos were collected on this jobsite on a daily basis.

Figure 7.6 A daily construction photolog compared with point cloud images.

Figure 7.7 Automatically monitoring operation-level details of construction progress requires assessment of building element appearance. Automatically recognizing steps in construction of concrete foundation walls requires image processing that can differentiate between insulation, waterproofing, and concrete.

Figure 7.8 A 3D image-based point cloud model of a rebar cage. Using 15 control points, the up-to-scale point cloud model is transformed into the site coordinate system.

Figure 7.9 Details that should be captured in craft worker activities to allow automated activity analysis from site video streams.

Figure 7.10 An example of a combination of computer vision and machine learning where the program is trained to learn certain behaviors over time.

Figure 7.11 To know what activity each person on site is engaged in—what tool they’re using, how long they are using it for—requires a massive database. Without detailed data, we won’t be able to develop proper machine-learning algorithms.

Figure 7.12 Moving forward, we will make greater utilization of the model for design, analysis, performance, and simulation throughout the building life cycle.

Chapter 8

Figure 8.1 We can help clients visualize problems. A building is just one outcome. Data visualization is another.

Figure 8.2 One of the major advantages BIM software has over data visualization tools is the ability to view data in three as well as two dimensions, helping clients better understand their data as it applies to the physical environment.

Figure 8.3 There are a number of really good data visualization applications on the market, such as Tableau and Spotfire. However, these products work best with 2D data.

Figure 8.4 BIM, a tool to document and manage the construction process, can also be used as a data visualization tool.

Figure 8.5 Dashboard for the Golden Gate National Parks Conservancy for the Project Frog modular building developed for Crissy Field Center. The dashboard tells a story about the building.

Chapter 9

Figure 9.1 CASE is a company founded on the notion that data is the medium of the building industry. Image by CASE. All Rights Reserved.

Figure 9.2 If you look at the latest trends in databases, they’re document-based databases rather than table or relational databases.

Figure 9.3 Each asset has its own page, presenting data that has been extracted from the BIM model. This includes the location of the asset, its unique ID, and manufacturer. This allows someone on site to access the data without needing to open up a BIM model.

Figure 9.4 Categories of assets are grouped together to improve discoverability. In this case, all the HVAC air-handling units are shown.

Figure 9.5 The HOAR FM Data Manager provides data on the assets within a building. From the home page, a user can search for a particular asset and bring up all the data in the BIM model pertaining to that particular asset.

Figure 9.6 After searching for “VAV,” the user is presented with all the HVAC variable air volume controllers within the building. The user can also search using a product name, description, serial number, or other associated data.

Figure 9.7 An early version of the CASE Building Analytics dashboard. The dashboard helps architects and building owners see trends in their projects’ geographic locations, sizes, and program types.

Figure 9.8 Solomon Cordwell Buenz Studio and Design Services infrastructure.

Figure 9.9 Dynamic area analysis.

Figure 9.10 Natural ventilation analysis.

Figure 9.11 Wind velocity analysis.

Figure 9.12 Therm heat transfer; color gradation and isobar analysis.

Figure 9.13 Therm heat transfer; color gradation and isobar analysis.

Figure 9.14 OpenAsset visual database.

Figure 9.15 Connected systems diagram.

Epilogue

Figure 10.1 In the future, buildings will increasingly be valued in terms of data.

Guide

Cover

Table of Contents

Preface

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