architecture | design | data

Architecture | Design | Data:Practice Competency in the Era of Computation

Phil Bernstein FAIA RIBA LEED AP©Associate Dean and Senior Lecturer, Yale School of ArchitectureAutodesk Fellow

AIA Project Delivery Symposium | Washington DC | 11 March 2019

Evolution of Tools and Technologies

Drawing CAD B I M

Drawing CAD B I M

Evolution of Tools and Technologies

Connected BIM

ConnectedBIMTechnologies

Big Data

Computational Design

Simulation / Analysis

Internet of Things

Industrialized Construction

Machine learning

Image Courtesy of Yale Center for Environment and Architecture


Big Data



Internet of Things


Machine learning


Big Data



Internet of ThingsIndustrialized Construction

Machine learning

https://www.johnsoncontrols.com/building-automation-and-controls/hvac-controls/control-sensors


Big Data



Internet of Things


Machine learning

cloud

mobile

big datamass storage

infinitecomputing

digital <> physical

computer-aideddesign

buildinginformation

modeling

industrialconstruction

iOT

realitycapture

algorithms

generativedesign

AR/VR analysis +simulation

machinelearning

Evolution of Connected BIM

Internet of Things


Big Data

Machine learning



projects history

testing,validation

bestpractice

alternativegeneration

controlsystems

assemblylogic

testingalgorithms

design logic

results data

projectstandards

projects archive

prototype development

performancedata

means and methodsprotocols

performancevalidation

assemblystrategies

Technologic Integration

Measurement àSimulation à Prediction

1 — introduction

2 — agency 2.1 The Digital Transformation of Design Connecting representation and design efficacy

2.2 Defining Design Intent, Precision and Results Connecting design information with execution

2.3 The Evolution of Responsible Control and Professional Care Changing professional standards and legal obligations

2.4 Preparing Digital Designers Integrating technical competency in professional education

2.5 Building Performance Design Integrating social obligations with the expectations of practice

3 — methodology 3.1 Procedures, Process and Outcomes Digital expansion of design potency

3.2 Information Control and Design Control Applying creativity to project information management

3.3 Optimizing, Solving, Selecting Applying tools for design optimization

3.4 Building Logic and Design Insight Making design information the basis for construction and building operation

3.5 Design Demands of Digital Making Implications of new materials and systems

3.6 Opportunities, Risks and Rewards Defining the architect’s responsibilities, fields of action and process obligations

4 — value 4.1 Creating New Value in Design Framing the objects and tasks of architectural design

4.2 Producing Design Process Interrogating scopes and instruments of service

4.3 Calibrating Results of Design and its Outcomes Defining measures of success

4.4 Design Value, Delivery and Efficacy Redefining financial success: fees and business value

5 — conclusion

6 — references

Agency Methodology Value

Current StateProfessional standards,

defining “intent”

Iteration, intuition, fixed deliverables

Commodification, lowest first cost

Future StateIntent à

execution, information

control

Systematic exploration, simulation,

analytic conclusions

Outcomes

2.3.2 BIM Execution Plan (BEP)

SMMP

ELECPLUM

FP

SMMP

ELECPLUM

FP

No clashes found

Sign-off

Publish clash log

Issues found

Update Models

COMP

1 Project Team revises and uploads models

2 Coordination Manager compiles notes and issues

3 Issues are reviewed at consultant coordination meeting

1Digital precision, optimization, simulation

2Information coordination and control

3 Solving, selecting

4Integrated lifecycle information

5Intention to execution, digital making

Process Opportunities of Technology


Digital simulation, big data and analysis allows architects to develop a design with higher levels of precision and insight and understand the technical implications of design decisions a priori.






2.1.2 Analytical formulae

Problem to be solved Example formula Note

Compute moment in a simple beam, uniformly loaded

5

Flow resistance of air through a small opening in an exterior wall

6

Required capacity of rainwater downpipes and gutters

7

5 American Forest and Paper Associatation, “Beam Design Formulas with Shear and Moment Diagrams,” http://www.awc.org/pdf/codes-standards/publications/design-aids/AWC-DA6-BeamFormulas-0710.pdf.

6 Axel Berge, Analysis of Methods to Calculate Air Infiltration for Use in Energy Calculations, Thesis, Chalmers University, 2011, http://publications.lib.chalmers.se/records/fulltext/147421.pdf.

7 Nedzink Company, “Determining the required capacity of rainwater downpipes and roof gutters,” http://www.nedzink.com/en/info-and-advice/roof-drainage-system/112/determining-the-required-capacity-of-rainwater-downpipes- and-roof-gutters.

wl

l

x

R R

Mmax (at center) . . . . . . . ="wl28

Sh(V) = p

0,845 ∙"A2∙ V

whereSh is the flow resistance [Pa"∙"m3/s]p is the density of the air [kg/m3]A is the area of the hole [kg/m2]

Qh = (a × i) × (ß × F)

a = the reduction factor for the rain intensity for flat roofsa = 0.60 flat roof with ballast of gravela = 0.75 for the other flat roofs

As flat roofs discharge the water at a slower pace, for all other cases (therefore all pitched roofs) applies a"="1,

i = rain intensity and is 1.8 (litre/minute)/m2ß = reduction factor for the roof width is determined by the pitch roofF = surface of the roof




Designing a building means coordinating both the physical artifact and the information flows necessary to generate it, including both geometry and metadata.





1 - Introduction

2 – Agency

2.1 The Digital Transformation of Design

2.2 Defining Design Intent: Depiction, Precision and Generation

2.3 The Evolution of Responsible Control and Professional Care

2.4 Preparing Digital Designers

2.5 Building Performance Design

3 – Methodology

3.1 Procedures, Process and Outcomes

3.2 - Information Coherence

3.3 Designing Design: Optimizing, Solving, Selecting

3.4 Building Logic and Design Insight

3.5 Design Demands of Digital Making

4 – Value

4.1 Creating New Value Through Design

4.2 Producing Design Process

4.3 Calibrating Design Values

4.4 New Values in The Systems Of Delivery

ConclusionImage courtesy Pierce Reynoldson, SKANSKA

3.2 - Is the creation and control of information systems by architects necessary for the design and construction of buildings?

3.2.6 Model coordination timeline for an airport project

Construction (As-builts)

Construction docs (CDs)

Design development (DD >50!%)

Design development (DD ≤50!%)

Schematic design (SD)

ARCH ARCHARCHARCH

STRC

MEP MEP

ARCH

STRC

MEP

AIR

UTL

LND

AIR

UTL

LND

AIR

UTL

LND

AIR

UTL

LND

AIR

UTL

LND

ENC SM

STLMP

VTELE

PLU

FPBAG

Space proving

DesignreviewSW

JVDJ

VSu

bcon

trac

tors

Fede

rate

d m

odel

Designreview

Designreview

Designreview

Design coordination Data Mgmt

Trade coordination Data Mgmt

As-built Mgmt Data Validation

Pre- Coord’n

Golden rules

Model validatn

BAGBAG BAGBAG PLU PLU

FP FP

ENC ENCENC SM SM

STL STLSTL MP MP

VT VTVT ELE ELE

Model

Data/Information

Process/Review

Document

ARCH – ArchitectureSTRC – StructureMEP – MechanicalUTL – UtilitiesAIR – Airside

LND – LandsideBAG – BaggageENC – EnclosureSTL – SteelVT – Vert. Transport

SM – LandsideMP – Mechanical PipeENC – ElectricalSTL – PlumbingVT – Fire Protection

2.3.3 The constellation of applications in use by SOM, 2007

Internet Component SearchTechtonic Network

Fabrication Information Model

Inventor Rhino SolidWorks Tekla ConcreaeTekla Steel

Radiance Ecotect Fluent Therm

Project WebsiteBuzzsaw/Constructure

Architectural

Digital Project

Building Development Information Model

Revit

Structural MEP

3D StudioRhino ExodusAffinityInnovaya

Translation Layer

Sketch Information Model

Rhino SketchUp Tran

slat

ion

Laye

r

Industrial Design

Imagine & ShapeThink3 Studio Tools

Translation Layer Translation Layer

Translation Layer

New

Del

iver

able

s Tr

ansl

atio

n La

yer

Model CheckNavisworks/xxx

RevitAuto CAD

Digital Project

E-Specs

Drawings

Paper

Internet Componant ProcurementGoogle

Traditional Deliverables

Visualization Quantification Building Performance Simulation & Analysis






Digital simulation and generative design, in combination, allows architects to more thoroughly explore options systematically, understand the results, and choose better solutions.




1 - Introduction

2 – Agency






3 – Methodology






4 – Value





Conclusion

3.3 - How do digital strategies for problem definition, generation, evaluation and optimization affect the architect’s process and goals?

3.3.3 Technology typologies

How are ideas translated from digital to physical form?

Construction planning, procuring, assembling

R E A L I Z A T I O N

3D printing Robotics CNC/CAMLaser screeningCommissioning

How are ideas simulated and evaluated?

Simulation, analysis, scripting, optimization

A N A L Y S I S

x

x

x

How are ideas defined and memorialized?

(Models, metadata, drawings)

R E P R E S E N T A T I O N

C O L L A B O R A T I O NHow are ideas captured, managed and shared?

Reference data Project record External knowledge

‘Tame’ design challenges ‘Wicked’ design challenges

Computing areas Choosing a design strategy for the overall organization of the building

Determining toilet capacities Making tradeoffs between ambiguous priorities

Coordination of structure and mechanical systems for clashes

Managing client expectations

Checking shop drawings for geometric conformance Composing the aesthetic expression of an element, space or building composition

Counting and coordinating electrical fixtures Translating design intent to the contractor

2.1.8 ‘Wicked’ vs. ‘tame’ problems

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.9447&rep=rep1&type=pdf





3 Solving selecting



The information that is the basis for design can support improved construction process and building operation, and creates a virtuous cycle of data that can be used to build insight and improve results.



1 - Introduction

2 – Agency






3 – Methodology






4 – Value





Conclusion

Image courtesy ALLOY Development

3.4 - Is architectural design representation obliged to support and integrate into post-design activities such as construction and building operation, and if so, how does that affect design generation?








The information that is the basis for design can support improved construction process and building operation, and creates a virtuous cycle of data that can be used to build insight and improve results.


Digital design information can drive automated construction process and logic, optimize means and methods and the resulting algorithms are instructive to developing and optimizing design ideas.


2.1.6 Technology categories and their evolution: BIM > Machine Learning

Technology Category In the era of BIM modeling In the era of machine learning

Representation Parametric models of geometry and metadata

Artificial intelligence-informed design through interlinked digital models

Analysis and Simulation Digital analytical models tied to scripts that test and choose results

Big data-based neural networks that predict complex outcomes

Realization Model-based simulation of construction yielding build-ready data

Information originating from the design process drives self-learning automated machinery on the project site.

Collaboration Web-based, social-media-enabled real-time connection and data exchange.

Real-time interaction enhanced by virtual and augmented reality supplemented with predictive collaboration through AI.

See also 2.1.4, p. 24

Risk

Valu

e

Low High

High

Mitigate Manage Embrace

Make fewer mistakes asindividual participants

Anticipate and reducemanageable risks

Make projects moresuccessful

Risk

Valu

e

Low High

High

Mitigate Manage Embrace

Anticipate, mitigate ownerchanges of scope

Accelerateschedule

Predict andmanage costs

Integrate tradecoordination

Predict project

outcomes

Reduce design andcoordination errors

3Operational

2Executional

1Transactional

4Aspirational

improved social conditions and advancing culture

improved performance of the final asset

improved construction means and methods

improved efficiency

Incr

easi

ng p

roje

ct va

lue

profi

t

cred

ibili

ty

influ

ence

=

=

=

=

Implications for the architect’s process Implications for the architect’s results

4.3.2 Values hierachy

Clients and Users

Builders

Designers

Transactional performance Executional performance Operational performance Aspirational Performance

Program conformanceDesign scheduleDesign quality

(Cost)

Project value

Budget conformanceSchedule

Built qualitySystems to spec

Durability

Systems performanceEnergy savings

CarbonStaffing optimization

Maintenance

Economic performanceEmployee satisfaction

Higher test scoresHealthier patients

Lower infection rates

Discipline optimization to smooth delivery

Inter-disciplinarydelivery optimization

Project operational optimization

Project performativityoptimization

3Operational

2Executional

1Transactional

4Aspirational

improved social conditions and advancing culture

improved performance of the final asset

improved construction means and methods

improved efficiency

Incr

easi

ng p

roje

ct va

lue

profi

t

cred

ibili

ty

influ

ence

=

=

=

=

Implications for the architect’s process Implications for the architect’s results

4.3.2 Values hierachy

Innovation (Leslie King, Esq.)

Business judgment rule? Contract for it?

Shareholders challenging the wisdom of a business decision taken by management must overcome the business judgment rule. . . . . For efficiency reasons, corporate decision makers should be permitted to act decisively and with relative freedom from a judge's or jury's subsequent second questioning. It is desirable to encourage directors and officers to enter new markets, develop new products, innovate, and take other business risks.” 1 A.L.I., Principles of Corporate Governance (1994) § 4.01(c) comment, p. 174

Aspiration à Operation

Intention Execution

CA

O

2.1.5 The evolution of project delivery models, 1970–2020

Design Build+

Flavored CM

Eco nomic context

High Interest Rates High Energy Costs

Liability Crisis Savings + Loan

Worldwide Downturn + Early

SustainabililtyDigital Design Data + Interconnectivity

Digital Fabrication + Big Data

Project deliverymodel

Evolution of design techno-logy

70s 80s 90s 00s 10s 20s

Tracing Paper

Layered Production

Computer-Aided Drafting

Objects

Building Information Modeling Connected Systems

!O

!A !C

!O

!A !CM

!O

!D !B

!O

!D !B

Design Bid

Build

Fast Track+

Original CM

!C

!O

!A

Integrated Design + Construction /Building Lifecycle

Measured Performance + Outcomes

!O Owner !A Architect !C Constructor !CM Construction !D Designer !B Builder Manager

Decade

OperationIntention Execution

CA OVa

lue

Time

Project value to Owner

Initiation

Construction starts

Occupancy

Agency of the Architect

Design begins

Permit issued

Need punchlist!

Statute of limitation runs

Measurement àSimulation à Prediction= Outcome-based Delivery

Firm profit margins, based on earnings before interest, bonus and taxes

Under 10% Weak operating margin

10%–15% Typical for most design firms

15%– 20% High margin firm, above average

20–25% Top performing firm, unusual margins

Above 25% Very rare, extraordinary performer

4.1.2 Design Firm Profitability

4.1.3 Distribution of fee typologies in the US

1.5%Precentage of construction cost not to exceed fixed amount

1.9%Fee per square foot

0.8%Other

20.4%Hourly rate (with or without agreed maximum)

28.7%Professional fee plus reimbursable expenses

36.1%Stipulated sum (fixed fee)

10.5%Percentage of construction cost

“We have to show that good design delivers on the core mission of our partner. It is quantifiable: reducing infections, making recovery times faster, and increasing staff retention.”

Alan Ricks, PartnerMASS DesignInterview in Yale School of Architecture CONSTRUCTS Spring 2018

MASS Design Proposal for Haiti Hospital

[email protected]

@Bernstein_ARCH

mailto:[email protected]

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