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PRESENTED TO THE
Commercial Remote Sensing - Applications for Bridge Management and Preservation
WESTERN BRIDGE PRESERVATION PARTNERSHIP MEETING
SACRAMENTO, CA
DECEMBER 1, 2010
EDD HAUSER, DIRECTOR
CENTER FOR TRANSPORTATION POLICY STUDIES
Commercial Remote Sensing - Applications for Bridge Management and Preservation
CENTER FOR TRANSPORTATION POLICY STUDIES
UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE
KELLEY REHM, AASHTO LIAISON
SHEN-EN CHEN, DEPT. OF CIVIL AND ENVIRONMENTAL ENGINEERING, UNC CHARLOTTE
Presentation Outline
� Commercial Remote Sensing (CRS) Overview
� RITA/ UNC Charlotte IRSV Project Overview
� 3D Terrestrial LiDAR Applications� 3D Terrestrial LiDAR Applications
� Small Format Aerial Photography Applications
� Impact on Bridge Preservation Activities
Commercial Remote Sensing (CRS)
� CRS refers to making and preserving images of infrastructure and
natural features from a distance using nonintrusive sensors; aerial
or terrestrial photography, LiDAR, RADAR and passive infrared, are
examples.
� For bridge health monitoring, CRS includes technologies as
inspection tools that are rapid, relatively safe, and cost-effective.
� Commercial satellites, airborne large format and medium format
optical photos do not have the resolution
(< 6 inch ), speed, nor cost-effectiveness for bridge SHM.
UNC Charlotte IRSV Project Objectives
� Research objective: develop an INTEGRATED REMOTE SENSING AND VISUALIZATION (IRSV) SYSTEM
PROTOTYPE - integrating CRS for bridge monitoring, management,
maintenance, & preservation.
� Outreach objectives: � Outreach objectives:
� 1) encourage high-level Commercial Remote Sensing (CRS)
technology applications for bridge management & preservation;
� 2) demonstrate such applications to industry-wide audience.
� Case Study Targets: Multi-span steel girder /concrete deck bridges
and all-concrete bridges over roadways, water, etc.
IRSV - Data Integration via Visualization
IRSV v. 1.0 (Interactive Large Screen Visual Analytics)
MULTI-COORDINATE VIEW GEO-SPATIAL VIEW
DOT TREND VIEW
DISPLAY CRITERIA SELECTION
DOT TREND VIEW
SINGLE BRIDGE TREND VIEW
IRSV v. 2.0 (Distributed Web-Based Apps.)
Geospatial View
Scatter Plot
Parallel Coordinates
Hierarchy of Variables
Data Collection and Analysis –
3D Terrestrial LiDAR
� Light Detection and Ranging System.
� Laser scanned images provide temporal database.
Terrestrial, Mobile, and Aerial LiDAR
LiDAR Scans -
Harnessing the power of CLOUD Harnessing the power of CLOUD
COMPUTING
What is Cloud Computing ?
Cloud computing is Internet - based computing,
using shared resources, software, and
information to link computers and other devices
on demand, like the electricity grid. on demand, like the electricity grid.
Cloud computing is a natural evolution of the
widespread adoption of 3-dimentional
virtualization, service - oriented architecture, and
utility computing
LiDAR-Based Bridge Evaluation Applications
� Image documentation
� Geometric changes
� Bridge clearance measurement
� Structural surface damage� Structural surface damage
� Bridge displacement measurement
� Blast impact monitoring
� Heavy truck impacts / Static load tests
� Traffic monitoring
Static Bridge Load Tests
Bridge Dead Weight Deflection
Girder 8 relative elevation curve comparison
LiDAR Scan Results
LiDAR Deflection = 0.663 in
FE Deflection = 0.605 in
Traffic Monitoring(Relative to bridge displacements)
Two Spans - Bridge
under Operational
Loading : Periodic
LiDAR Scan
Case 1 (at 8:42 am)
263 Cars, 9 trucks263 Cars, 9 trucks
Case10 (at 1:03 pm)
190 Cars, 0 trucks
Tie Traffic Operations to Bridge Performance
Spatially Integrated - Small Format Aerial Photography
SI – SFAP
� Cessna C210L plane
� Cannon 5D DSLR camera
� Approx. 1000ft altitude� Approx. 1000ft altitude
� Orthogonal rectification not needed
Large FormatLarge Format SI-SFAPSI-SFAP
Large Format vs. Spatially Integrated - Small Format Apps.
SI-SFAP Bridge Evaluation Applications
� Construction monitoring
� Disaster response and clean-up monitoring
� Image documentation
� Geometric changes over time� Geometric changes over time
� Bridge deck cracks, expansion joints, spalling
� Environment studies in area around bridge
� Site Planning
� Blast Impact Monitoring
� Truck / Traffic Monitoring
Project Construction Monitoring
Deck Crack Monitoring
Joint Movement and Deterioration
Benefits of CRS to Bridge Preservation
Bridge Preservation: TSP-2 Objectives
� Support and expand AASHTO and TRB activities
� Engage broad support – practitioners, industry,
academic and other research institutions
� Develop a specialized Technology Transfer Clearinghouse for
bridge management and preservation
� Enhance coordination for these objectives, and
� Support the Regional Partnerships
FHWA Objectives for Bridge Preservation
� Provide technical support as part of a systematic process
� Better define how needs are identified, prioritized, and
programmed
� Identify resources needed to reach the Goals
� Quantify the cost-effectiveness of preservation activities
� Track, evaluate, and report on progress toward the Goals
Definition of Bridge Preservation - BPETG
� Preventive or condition-driven actions or strategies that prevent,
delay, or reduce deterioration
� Restore the function of existing bridges
� Keep bridges in good condition
� Extend useful bridge service life
� Source: Bridge Preservation Expert
Task Group, 10/15/2010
Finally!
� Through our research at UNC Charlotte in developing the IRSV
Prototype, and in our initial efforts to extend our outreach as a
partner in the process we’ve been discussing, we can suggest that
these two Commercial Remote Sensing technologies - LiDAR these two Commercial Remote Sensing technologies - LiDAR
and SI-SFAP - appear to have the potential for becoming cost-
effective solutions to help bridge engineers do their work.
ACKNOWLEDGEMENTS
� USDOT / RITA grant DTOS59-07-H-0005 (Mr. Caesar Singh)
� NC advisors: Moy Biswas, NCDOT; Garland Haywood Division 10 NCDOT; Jimmy Rhyne, City of Charlotte DOT
� National advisory committee: Phillip Yen (FHWA), Sreenivas Alampalli (NYSDOT), Dan Turner (U.Alabama), Ahmad Abu-Hawash (IowaDOT), (NYSDOT), Dan Turner (U.Alabama), Ahmad Abu-Hawash (IowaDOT), Rudy Rivera (Los Angeles Co. DPW)
� Research Team: Edd Hauser, Shen-en Chen, Xiaoyu Wang, Rashna Vatcha, Chris Watson, (UNC Charlotte); Ron Eguchi and Z. Hu (ImageCat Inc.); Howard Chung (Acellent Technologies); Charles Boyle, Boyle Consulting, Inc.; C. Michael Walton, & Kelley Rehm
� The views, opinions, findings and conclusions reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT, RITA, or any State or other entity.
Questions
Thank you!
Questions? Questions?
Edd Hauser - [email protected]
University of North Carolina at Charlotte
www.transpol.uncc.edu