From VDC to the Digitalization of Construction Martin Fischer Professor, Civil + Environmental Engineering and (by courtesy) Computer Science http://www.stanford.edu/~fischer [email protected] § Director, CIFE (Center for Integrated Facility Engineering) § Foreign Member, Royal Swedish Academy of Engineering Sciences

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• 100% funded by industry – Building owners – Design and construction companies – Software and hardware vendors

• 1988-2000 – Building Information Modeling (BIM)

• 2000-2010 – Virtual Design and Construction (VDC)

• 2010+ – Optimize Facility Performance

VDC Certificate Program Graduates Award Ceremony on Sept. 6, 2016 in Sola, Norway

Construction labour productivity has not kept pace with overall economic productivity Last Modified 8/15/2016 5:47 PM Pacific Standard Time

Labor productivity (gross value added per hour worked, constant prices1)

$57 trillion of infrastructure investments required by 2030 (McKinsey Global Institute)

Printed 8/12/2016 9:10 AM Central Standard Time

1 Based on 2010 prices SOURCE: OECD Industry Stats (http://stats.oecd.org)

Data from Roberto Charron, McKinsey and Paul Teicholz, CIFE

McKinsey & Company

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Challenges affecting the construction sector

Printed 8/12/2016 9:10 AM Central Standard Time

External challenges

Last Modified 8/15/2016 5:47 PM Pacific Standard Time

Internal challenges

▪ Shortfalls in accountability ▪ Talent management ▪ Reinventing the wheel ▪ Failure to adapt to new technology ▪ Coping with complexity ▪ Fragmented value chains ▪ Extensive subcontracting ▪ Complex portfolios ▪ Competitive pressure

Data from Roberto Charron, McKinsey

McKinsey & Company

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We still produce too much rework …

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… and work too inefficiently

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Would your projects benefit if … Everyone is working on the right tasks at the right time all the time.

We are designing what the client wants.

We will be productive as possible next week.

We are We are certain We are sure that we are installing building everything safely that everything and with the best everything fits. fast and methods. right the first time. We are installing everything We gave the accurately based on the latest, client exactly correct information. Paper-free. what he ©2016

wanted.

What capabilities have really changed in the last decade? Digital modeling

Amount of data we can handle

Communication ©2016

Pictures courtesy SPS, Beck, and DPR

Illustrations of good VDC practice

Pictures courtesy DPR

©2016

Would your projects benefit if … Everyone is working on the right tasks at the right time all the time.

We are designing what the client wants.

We are certain We are sure that we are build everything safely that and with the best everything fits. methods.

We are installing everything accurately based on the latest, correct information. Paper-free. ©2016

We will be productive as possible next week.

We are installing everything fast and right the first time.

We gave the client exactly what he wanted.

VDC method to give the client everything he wants Open whole scope of hospital on budget and 30% earlier than typical Highly reliable construction

Confirm constructability of detailed design

Everyone works with the same plan

Combine everyone’s detailed design ©2016

VDC Overview Client Business Objectives Project Objectives

Integrated Concurrent Engineering (ICE)

Project Production Management

Product Modeling BIM++ ©2016

WHY is the client doing the Client/Business Objectives project?

WHAT does the project team need to achieve?

HOW is the project team accomplishing the project objectives?

BUILDING PERFORMANCE • Usable • Operable • Sustainable

Project Objectives

PROJECT PERFORMANCE

Integrated Concurrent Engineering (ICE)

PROCESS PERFORMANCE

• Buildable Safety, Budget, Schedule, Quality

• Production metrics

Process Modeling Production • Controllable factors of production Management

Product Modeling BIM++

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Example of Application of VDC on a Multi-Family Housing Project in Lima, Peru from the CIFE-SPS VDC Certificate Program Implementation Phase

Vivienda Multifamiliar “Residencial Varela” Monthly Report N° 6 Date: 3 May. 2016 Javier Otiniano Project Manager PROYEC Contratistas Generales S.A.

©2015

USING THE BIM FOR INSTALLATION OF PRECAST SLABS IN BASEMENT CEILING (FLOOR PARKING)

PRECAST SLAB MODULES ACCORDING TO THE SECTORIZATION “7 SECTORS”

-

©2015

INSTALLATION TIME OF PRECAST SLABS: 8-10 min c / u INSTALLATION TEAM: 3 People

ECONOMIC BENEFIT / ITEMS FOR USE OF PRECAST SLABS

31% savings in structure cost

$. 8,430.00

9000 8000 7000 6000 5000 4000 3000 2000 1000 0

$. 5,411.00

$. 15,956.00

$. 1,742.00 $. 373.00 Concrete Savings

Steel 1/4" Savings

Joist and Flooring Savings in Plastering, Blocks Savings painting, ceiling (basement ceiling)

(-)

DIRECT COST FOR USE OF PRECAST SLABS 10000

$. 8,873.00

8000 6000

$. 11,032

4000

$. 2,159.00

2000 0 Lightened Precast Slabs

Solid Precast Slabs

ECONOMIC BENEFIT FOR USE OF PRECAST SLABS

$. 4,924.00 ©2015

WORKING MEETINGS WITH TECHNICAL TEAM

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EVALUATING THE 4D PLANNING MEETING, WITH TECHNICAL EQUIPMENT ON THE CONSTRUCTION SITE

PROPOSAL FOR IMPROVEMENT MEETING, WITH THE DESIGNER OF THE GAS SYSTEM AND TECHNICAL TEAM

©2015

IMPLEMENTATION VDC

MANAGEMENT, PLANNING, AND PRODUCTION PROCESSES TO ACHIVE THE TARGET

IMPROVEMENT PROJECT

4 D MODEL

SECTORIZATION/WORK TRAINS MAPPING PROCESSES I C E S E S S I O N S

Confirm attendance

DESIGNERS

Identify deficiencies in design

Contributions to improvements

Check queries

Convene Meeting

Present Schedule

Contributions to improvements

PROJECT MANAGER

Decision Making

YES

Review by levels and specialities

BIM SUPPORT

BIM MANAGER

COLLABORATIVE MEETINGS

Develop Modeling Plan

Model Presentation

Identify deficiencies in design

Make queries of the Model

Contributions to improvements

Contributions to improvements

NO

Specify contributions

R E S I D E N C I A L

VARELA REDUCE CONSTRUCTION TIME BY 5% - TARGET GOAL

OPTIMIZED SCHEDULED

CREATE VALUE CUSTOMER ©2015

Making of improvement metrics

Key technology and management developments Mobile • from just-in-case to just-the-right information Cloud • anytime (push and pull, bi-directional, ”unlimited”) Parallelization • fast Location / dimensional measurement • accuracy, dimensional control, off-site / on-site Machine learning • experience and data Robotics, additive manufacturing • virtual ßà real, safety, environmental impact Internet of Things (IoT) • virtual ßà real Virtual Environments • test! Collaboration • concurrent knowledge Lean • lower uncertainty, lower risk, customer, pull, purpose à value ©2016

Wake-up Calls The following slides show several innovations that show dramatic improvement opportunities.

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Daily Process & Feedback Loop START OF DAY

u Reduced data-entry (only once at start and end of day) u Minimal Training required

Anirudh Reddy

u All data and activities verified by Device based Locational and Transactional Data, in the context of the project plan and operations

• Create Material Requests for planned • Collect Material at work on SiteMan App Plant • Write Requests on • Data captured by Smart Card Cabin Controller and • Verify at Weigh-Station Vehicle Unit • Data captured by Cabin Controller and Vehicle Unit

• Whole day’s activity visualized and analyzed on Software portal • Work plan for next day created based on recommendations by Einsite

END OF DAY

• Drop material at worksite, and perform work using relevant machinery • Acknowledgement made by supervisor ID card on • Enter Daily Progress device Reports • Entries cross checked with device Data

einsite

Fully automated design of a house from a sketch (elilbre)

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REDUCING THE COST OF STEEL STRUCTURES USING C O M P U TAT I O N A L D E S I G N O P T I M I Z AT I O N

Wo rk by Fo rest Flager in Co llabo ratio n with Arup and John Haymaker CASE STUDY RESULTS DESIGN PROBLEM

Objective: Minimize steel weight Constraints: Safety and serviceability

conventional design method

FCD (128 cpu) design method

4 hrs

3 sec

39

12,800

216 hrs

151 hrs

2,728 met t

2,292 met t

-

$4 M (-19%)

Variables: 1955 size and shape variables Possible design alternatives: ~ 102435

BiOPT METHOD http://cife.stanford.edu/sites/default/files/TR202.pdf

PROCESS Design cycle time Alternatives evaluated

GEOMETRIC MODEL GEOMETRIC MODEL 1

Total design time PRODUCT

ANALYTIC MODEL 2

Total steel weight OPTIMIZE SIZING

Est. cost saving (USD)

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OPTIMIZE SHAPE

4 SEQOPT Algorithm (Booker, et al. 1999) FCD Sizing Algorithm http://cife.stanford.edu/sites/default/files/TR201_0.pdf ©2015

• Orders of magnitude reduction in design cycle time • Evaluation of a greater number of design alternatives • Improved product quality

A Tale of Two Moment Frames KEY

3 Continuity Plates

W14X233 W14X176 W14X132 W24X117 W18X86

Doubler Plate

Steel Weight Total Cost Procurement Time

Original Frame

Value-Engineered Frame

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+8% -13% -20%

Work by Forest Flager, Pratyush Havelia, Henry Hamamji, Filippo Ranalli, Bo Peng, Thomas Trinelle In collaboration with SOM, Herrick, Autodesk CIFE 2016

Copyright Ó 2016

Steel Frame Cost Components Design Cycle: 8-24 weeks SUPPLY CHAIN EXPERTISE

DESIGN DECISION BASIS

Other Material 27% Erection Fabrication

CIFE 2016

Copyright Ó 2016

Automatic as-built BIM from laser scans

In collaboration with Iro Armeni, Silvio Savarese, Amir Zamir, and Ozan Sener

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“The automated execution of processes changes everything.” (Alan Perlis, 1961)

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Flow-based Construction Site Management

IN

N E L LY G A R C I A - L O P E Z C O L L A B O R AT I O N W I T H G R A Ñ A Y M O N T E R O

September 2016

Case study: Applying the flow-based site management method Project info: • Graña y Montero jobsite in Peru • 11 basements + 21 floors • 18-week period (8 weeks on site) • Structural phase Objectives: 1. Can the flow-based model represent the look-ahead plan? 2. Does the method help field managers make decisions during look-ahead and daily planning?

Case study project adopted best practices for production planning

Master plan • Processes • Gross constraints

Takt plan • Sector definition • Quantities • Trade sequence • Crew balancing

Look-ahead plan • Constraints analysis • Productivity • PPC + reasons

Daily plan • • • •

Quantities Productivity Daily PPC Visual planning

19th floor

Example of daily plan Activities

Crew (type & number)

Time

Risk

18th floor

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So, why was it so difficult for the project team to stick to the plan?

Video link: https://youtu.be/sT6kkvE2WXU

Activities require a set of flows to start execution Koskela (1999)

Flow Key: Labor

Workspace Precedence Materials/components Information Equipment External (Permits, inspections)

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Activity

Existing construction models do not formally represent, track, or quantify the activity flows Existing transformation view What flow? Build deck form A1

Install slab rebar A1

Pour slab A1

What caused delay? ∆

Build deck form A2

Install slab rebar A2

Pour slab A2 How are these flows affected?

Did one of these flows fail? By how much? Was it something else? Are we representing all the flows?

Existing construction models do not formally represent, track, or quantify the activity flows Proposed flow view What flow? Build deck form A1

Install slab rebar A1

What caused the delay?

Pour slab A1 ∆

Flow Key: Labor

Build deck form A2

Install slab rebar A2

Precedence

Information

Did one of these flows fail? By how much?

Equipment External (Permits, ispections) 36

Pour slab A2 Steel crew availability

Workspace

Materials/components

Concrete crew idle

Are we representing all the flows?

How are these flows affected?

Weekly data collected by the app Aggregate metrics

Activity metrics

Flow metrics

WIP

Activity info

Flow info

Aggregate activity statuses

Activity execution variability

Flow execution variability

10 variables

17 variables

24 variables

High level of detail data about the plan, its execution, and reasons for variability

GyM case study data (18 weeks) Total

Per week

# Activities

1,153

64

# Flows

4,192

232

415,008

23,056

# Data points

Large dataset for supporting:

Performance analytics

Predictive models (data mining + machine learning

Total additional data collection effort: 45 hours

Feedback from field management team “The analytics allow me to dig deeper into the design of the production system … [and] which teams have the best and worst performance.” Project Superintendent “Keeping track of the historical flow performance is key. We might have a hunch about what flows are consistently late, but we don’t have the data to identify performance issues.” Project Engineer “It’s very useful that we now have a tool that formally maps the flows that are needed to execute an activity … we think about these things, but there is no formal tool that allows us to check that all the flows are ready so the activity is not in danger.” Project Engineer

It’s not experience OR data it’s Experience AND data ©2016

I have made all my generals out of mud. Napoleon

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Interact in a virtual environment

Screenshots courtesy Brandon Fischer

• Rapidly understand target, team, roles, progress, obstacles, challenges • Frequent communication, feedback

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Scaffolding

70kg/100kg Tor

Gunnar

200kg/150kg Steinar

82%/100% 1,640kg / 2,000kg 54 #8, 5m

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The future looks different, but the future is now Strategic Implications Ø Projects vs. Corporate Ø Staff Development Ø Partners

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Do you want to continue to be a player or become a spectator?

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