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On-Going NCHRP Construction Research
Doug Gransberg, PhD, [email protected]
AASHTO Subcommittee on ConstructionAugust 2016
1
© 2016, All rights reserved, Douglas D. Gransberg
Active NCHRP ProjectsNCHRP Title PI Status
19-10 AASHTO Partnering Handbook, 2nd ed. Gransberg – ISU 2nd year
24-44 Geotechnical Risk Management Guide for DB Projects
Gransberg – ISU 2nd year
08-104 Contracting Strategies Guidebook for Administration of Concurrent Regional Emergencies
?? Pending
08-107 Post-Award Contract Administration for Alternative Contracting Methods
Molenaar - CU 1st year
20-07 (373)
Utility Coordination Using Alternative Contracting
Gransberg - ISU 1st year
47-02 AD/AB Pavement Type Gransberg - ISU Complete
47-06 Optimizing Program Delivery Methods Tran – U Kansas Complete
47-09 Practices for Establishing Contract Completion Dates
Sturgill -UKY On-going
2
Construction Research - Present• Alternative project delivery• Risk management• Collaboration• Integration• Process- Oriented
4
Construction Research Future• Institutionalize collaborative business
practices• Visualize construction in virtual space• Use private financing• Artificial neural networks• Exploit data• Information-Oriented
5
Exploit and Leverage Data
• What information is needed?• How can information add value in real-time?• How do we do more work with the current
amount of resources?THE RESEARCH COMMUNITY NEEDS DIRECTION
FROM YOU!
6
Data – Information - Decision9
Active PathInactive PathNon-Existing Path
Planning Phase
Design Phase
Bidding Phase
Construction Phase
Operation Phase
DMA DMB DMN
I1N I21 I22 I2n Im1 Im2 Im3 ImnI12
D11 D12 D13 D14
I11
D1n D21 D22 D23 D2n Dm1 Dm2 Dm3 Dmn
Decision
Information
Data
…..
DATABASE I DATABASE II DATABASE N…………….
…..
…..…..
…..…..
….....
Legend :
Potential applications
• Unit Price Space Maps• Automated As-built Schedule Development• Process Map / Exchange Requirement Matrix
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Deep Data Analytics of Unit Price Data
11
Table
Text
Analysis
Geoprocessing
Conversion
Cleaning
Data Selection
Pattern, And Stat
Processed Data
Spatial Data
Iowa DOT Bid Data
Spatial Interpolation Map
Shapefiles
Bid Tabulation Data GIS Spatial Analysis
Unit Price Trend Analysis
$-
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
7/6/20091/22/20108/10/20102/26/20119/14/20114/1/201210/18/20125/6/201311/22/20136/10/201412/27/20147/15/2015
$/to
n
Let date
Unit cost over time
Unit cost ($/ton) Linear (Unit cost ($/ton))
12
Deep Data Analytics of Daily Work Report Data
• Since late 1990s, Digital Daily Work Report(DWR) Systems have been developed and used by State highway agencies
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General Information
•Project ID•DWR date•Work suspension and resume
time•Presence of contractor•Day charging•Approval
Work activities
•Project ID•DWR date•Work item•Quantities of work
performed•Location•Contractors performing the
work
Weather information
•Low and high temperature, •General weather (sunny,
cloudy, wind etc.)•Rainfall•Ground condition (dry, wet,
hard to work)
Equipment
•Euipment name/type/id•Number of equipment•Hours used
Labor
•Labor type•Labor number•Labor hours
Remarks
•Significant communications with the contractor
•Significant events•Delay cause
As-built Schedules• Shows actual
sequences and durations of construction activities
• Takes account of change orders and schedule changes from the originally planned schedule
• Project level vs activity level as-builts
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Project level
Activity level
To date FinalBy TimeBy Level
Project control Delay claims
Bar chart
Cumulative quantity chart
Visualization
Uses
Categorization
As-planned schedule
Planning Construction
As-built to date schedule
Final as-built schedule
Construction begins
Construction completes
Post construction
Time
ABSS – Activity level ABS
• Detailed project control
• Identify dates when productivity was low
• Predict time to complete the task based on the current productivity
• Manage inspection resources
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0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Qua
ntity
of c
oncr
ete
ditc
h pa
ving
(S
QYD
)
Date
December 2012
Constant productivity line
January 2013
Summary
• Analog -> Digital (transformative changes)• Deep data analysis is possible• Better and smarter decision making and
project delivery
22