Transcript Statistical Identification of Items Important for

Adaptive 3D Imaging and Change Analysis of
Civil Infrastructures
Pingbo Tang1, Chengyi Zhang2, Jose Diosdado3, Ram Ganapathy3
1Arizona
State University
2Eastern Kentucky University
3DPR Construction
North America-East Asia Workshop on Big Data Analytics for Infrastructure
and Building Sustainability and Resilience (IBSR) Research
Beijing, China, P.R., September 19-21, 2014
Before I start talking about data
science and technologies…
Great but complicated technology will not ensure high-quality
data and information
Professional Camera
Smart Phone Camera
Technical Complexity
35
30
25
20
15
10
5
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Required Expertise
http://phandroid.com/2013/11/25/androidcamera-api-burst-mode-raw-support-comingsoon/
Problem Complexity
Big Data Analytics for Infrastructure and
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What is the problem?
Change management and quality
control of multi-trade collaboration in
construction
Forty percent of U.S. bridges were built over 40
years ago, with a design life of 50 years.
• Over 500 failures of bridge structures occurred
in the U.S. between 1989 and 2000.
• More than 30 bridge collapses in China from
2007 to 2010 that killed 140 people in total, and
injured 126
• How to monitor them?
• How to repair them?
• How to reduce the socioeconomical impacts?
Understand the tolerances of
prefabricated components,
geometric changes of structures
• How these changes happen?
• How they influence each other?
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• Which changes are dangerous?
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A Data Collection Technology for
Tolerance and Change Analysis
• Surveying based physical
measurements
– Tape, rod, total station
– Measure actual facilities
• Laser scanning
– Capturing thousands of
points per second
– Densely sampling of
scanned object (1cm
within 50 m)
– Measure 3D as-built
models of facilities
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(Rudolf Staiger, 2003)
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Other Applications of Laser Scanning
Technology in Civil Engineering
• Progress monitoring
• Quality control
• Construction workspace
modeling …
(Cho et al., 2013)
(Gordon & Akinci, 2005)
(Rudolf Staiger, 2003)
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(Turkan,
Building Sustainability and Resilience (IBSR)
Research
Bosche, & Haas, 2010)
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Challenges of Using Laser Scanning in
Tolerance and Change Analysis
• How to collect sufficient 3D data for assessing tolerances
and analyzing changes?
– What is “sufficient”?
• Domain requirements
• Geometric complexity
• Arrangements of building components
• How to ensure timely data collection, data processing, and
deliveries of required information?
– Planning the collection and processing workflows of 3D data
• Tens of steps of data processing
• Hundreds of data processing parameters that need engineers to
decide based on their experiences
• The data processing time and quality can vary significantly person by
person
• Exponentially large search space of data collection and processing
plans
Big Data Analytics for Infrastructure and
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What is “Sufficient” Data?
• Depending on what you need
GSA
Level
1
2
3
4
LOA (Tolerance), mm (inch)
±51 (±2)
±13 (±1/2)
±6 (±1/4)
±3 (±1/8)
LOD (Data Density), mm × mm
(inch × inch)
152 ×152 (6 × 6)
25 × 25 (1 × 1)
13 × 13 (1/2 × 1/2)
13 × 13 (1/2 × 1/2)
• Depending on the geometric complexity of objects
• Depending on how the components connect to each
other and how the errors accumulate
Big Data Analytics for Infrastructure and
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How to Ensure Timely Data Collection
and Processing?
Recognize
Superstructure
Laser-Scanned
Data of a Bridge
Random
Location:
100 times
Choose One Constraint
Construct
Surfaces of Two
Objects
Identify Objects
Below the
Superstructure
Grid
Location:
(1m, 1m)
Sample
Surface
Points
Recognize
Highway
Minimum
Extract Vertical
Distances at
Sampled Points
Data processing and
interpretation
Objectives of
data collection
Which
method?
Imaging
planning
(some images from: Huber et al., Leica, FARO, UCSD, Michael Baker Jr., Inc.)
• Domain
Requirements
• Geometric
complexity
• Site Model
GSA
Level
1
2
3
4
LOA (Tolerance), mm (inch)
±51 (±2)
±13 (±1/2)
±6 (±1/4)
±3 (±1/8)
LOD (Data Density), mm × mm
(inch × inch)
152 ×152 (6 × 6)
25 × 25 (1 × 1)
13 × 13 (1/2 × 1/2)
13 × 13 (1/2 × 1/2)
Source: General Services Administration (GSA) 3D/4D BIM Guide Series
Big Data Analytics for Infrastructure and
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Dynamic Programming for 3D Imaging
Planning
• Using Graphs to represent features and spatial
relationships between them
• identify “clusters” of features for less
computational expensive 3D imaging planning
• Combine “local” solutions to approximate the
solution of the complete network of features
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Adaptive Imaging and Data Processing
Data
Spatial Pattern Analysis Methods
Adaptive 3D Imaging Methods
• 3D Data Quality-Time Analysis
• Sensor models that quantify trade-offs between data collection
time and the data’s Level of Accuracy (LOA) and Detail (LOD)
• Spatial Deviation Computation
• Algorithm performance models that quantify the impacts of data
processing options (e.g., directions of deviations) on the datamodel deviation LOA/LOD and the data processing time
• Geometric Complexity Analysis
• Algorithms that identify the needed LOA and LOD for capturing
geometric details/complexities of the given shapes and
tolerances
• Imaging Sensor Space Planning
• Graph-based planning algorithms that automatically generate
imaging locations and parameters for capturing data of given
LOA and LOD within time limits
• Adaptive 3D Imaging Mechanisms
• Spatial Deviation Classification
• 3D registration and computer vision methods that recognize the
types of tolerances causing a given deviation pattern
• Tolerance Parameter Derivation
• Algorithms for deriving parameters of camber, sweep, twist,
dimension errors from spatial deviations
• Tolerance Network Analysis
• Tolerance network models for analyzing relationships between
tolerances for virtual “mock-up” and placement planning
• Imaging sensor control methods that automatically adjust the
imaging parameters according to geometric complexities
New Objectives of Data Collection and Processing
Deflection
Sweep
(a)
(b)
(c)
Big Data Analytics for Infrastructure and
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(d)
(e)
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What’s Next?
• Analyzing tolerances
Node:
Feature, Datum,
DRF, and/or
Datum Feature
Connector:
Indicates a datum to
feature relationship
Indicates the orientation variation of
the feature from the connected datum
about the X-axis of the features DRF,
+ follows right hand rule
Indicates the location variation of the
features origin from the connected datum
along the X-axis of the datum DRF
• Tolerance control strategies
XO
YO
ZO
XL
YL
ZL
• Workflow optimization
F
W
Datum
Orientation
and Location
Tolerance
Frame
Form
Tolerance
Frame
Shading indicates the datum priority for
controlling this tolerance of the feature.
It also specifies this features priority in
specifying the DRF for the component
for which this feature is a part.
Dark shading for primary, light for
secondary, and none for tertiary
XO
YO
ZO
XL
YL
ZL
Is a datum for
F
Feature
W
Indicates the allowable form variation
of the feature from the connected
datum
Indicates the allowable waviness
variation of a feature superimposed
over the form
– Exponentially large combinations of data
collection and processing options
– New optimization methods
* The MMVC and LMVC boundaries are of the same type as and parallel to
the feature’s nominal geometry. The location of the boundaries are such that
they contain the maximum deviations resulting from the combination of all the
tolerance slots with the M or L specification. The MMVC is the boundary in
the direction that increases the amount of material in the component for
which the feature is a part. The LMVC is the other boundary.
A lone 0 in the slot indicates that, the location of the
feature’s origin in the x-axis direction of the datum’s DRF
+.1
Laser-ScannedM
(for this slot), is determined by contact of the feature
with
Data of a Bridge
the connected datum
0
Grid
The M or L after
the value indicatesRandom
that, the location variation, of
Recognize
Superstructure
the origin ofLocation:
the nominal geometry,Location:
in the z-axis direction of the
(1m, 1m)
100 times
±.1
.001
The form variation of the feature can be ± .005 units from and
Bigmeasured
Dataperpendicular
Analytics
Infrastructure
and
to thefor
feature’s
substitute elementIdentify
Objects
Below the
Building Sustainability and Resilience
(IBSR)
Superstructure
Research
This is the basic symbol for a slot. For this slot it means the
orientation of the feature’s substitute element from its nominal
geometry can be rotated ± .05 degrees about an axis, parallel to the
datum’s DRF z-axis, through the origin of the feature’s nominal
geometry
M
±.05
±.1
M
datum’s DRF (for this slot), is limited by the boundary defined by
the maximum material
condition (MMVC) or Least
Choose virtual
One Constraint
material virtual condition (LMVC)*
Sample
Construct
Surfaces of Two
Objects
Recognize
Highway
Surface
Points
Minimum
A blank slot indicates there is no tolerance of this type and
direction specified for
the feature
Extract
Verticalfrom the connected datum
Distances at
Sampled Points
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Big-Data Research 1: Correlated Spatial
Change Pattern Analysis
Obj. 1: Change Detection
Matching Similar Spatiotemporal Patterns in Data
Countermeasure
Analysis and Further
Change Monitoring
Data Deviations (Changes)
Obj. 3: Change-Based Collapse Prevention
Obj. 2: Change Pattern Analysis
Correlation of Change Patterns and Risks
Change Classification, Correlated Change
Analysis
•Deterioration rates
•Structure collapses
Correlated
Changes and
Change Patterns
Deflection
Change-based Predicators of Collapses
An example of change
patterns: correlated
dislocations of girders
Sweep
Warped Rafters
Settlement
21.36 m
21.32 m
Big Data Analytics for Infrastructure
Radius of theand
tank along (IBSR)
Building Sustainability and Resilience
Roof Slope Variations due to Warped Rafters Research elevation
3.59°
1.17°
3.63°
Discovering general principles
of changes from data of
thousands of structures …
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Big-Data Research 2: Collaborative
Data Processing History Analytics
For safety margin analysis of civil infrastructures
Data Processing Histories:
H = 𝑊1 , … , 𝑊𝑛
Integrated Workflows:
Workflow Patterns:
P = 𝑃1 , … , 𝑃𝑚 = 𝑀𝑖𝑛𝑖𝑛𝑔(𝐻)
Obj. 1 – Workflow Pattern Detection
Obj. 3 – Safety Margin Characterization
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑡 = 𝐴𝑠𝑠𝑒𝑠𝑠𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦(𝐷𝑎𝑡𝑎, 𝑊𝑜 )
𝑊𝑜 = Plan(𝑄𝑐 t , Data t , P)
Obj. 2 – Workflow Generation
Decay Models of Safety Margins:
𝑀𝑎𝑟𝑔𝑖𝑛 𝑡, 𝑓𝑎𝑐𝑡𝑜𝑟𝑠
= 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑡 − 𝐷𝑒𝑚𝑎𝑛𝑑 (𝑡)
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Summary and Future Research
• The emerging importance of coordinating data
collection and processing workflows with site
conditions and constraints
• Scientific challenges: 1) theoretical foundation for
automatically determining objectives of data
collection; 2) new computational models for
rapid planning of data collection and planning
workflows
• Future Big-Data directions: 1) correlated spatial
pattern analysis; 2) harvesting and managing data
processing histories of engineers and scientists
Big Data Analytics for Infrastructure and
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The basketball court cleaner
Sweat
Can fall
Floor get wet
http://www.sweatblock.com/images/excessive-sweating.jpg
http://www.nba.com/multimedia/photo_gallery/1204
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Adaptive 3D Imaging and Change
Analysis of Civil Infrastructures
Pingbo Tang
[email protected]