Road Asset Management
Road Asset Management
Raja Shekharan, Ph.D., P.E.
Virginia Department of Transportation
National Workshop on Modern Trends in Pavement Engineering
IISc, Bangalore July 15, 2011
Transportation Asset
Management - Definition
“Transportation Asset Management is a
strategic and systematic process of operating, maintaining, upgrading and expanding physical assets effectively throughout their life cycle. It focuses on business and engineering practices for resource allocation and utilization, with the objective of better decision making based upon quality information and well defined objectives.”
Transportation Asset
Management
A business model, a decision support system and a management approach that addresses
(AASHTO Transportation Asset Management Guide):
• What is the current state of physical assets?
• What are the required levels of service and
performance delivery?
• Which assets are critical to sustained performance?
• What are the best investment strategies for
operations, maintenance, replacements, and improvement?
Asset Management
Methodology
• Manage assets using a life-cycle analysis approach
• Use a Needs Based Budget approach to identify and prioritize maintenance and
operations needs based on the inventory and condition assessments
• Employ processes to plan, budget, implement, monitor and measure performance
Bridges
Maintenance
The focus of maintenance is on physical assets
Interstate, Primary, Secondary Roads
Tunnels Rest Areas Roadside
Operations
The focus of operations is on movement of traffic
Cameras
AASHTO Guide to Transportation
Asset Management
Asset Management Program
The Business Process
Incremental Development
Methodology
Also known as • Spiral • Iterative • Evolutionary • Progressive • Extreme • RUP (IBM) • etc.Budget Process Cycle
Data Collection Analysis Needs-based Budget District Allocations Work A ctivities PlanningModules of Asset
Management System
•
PMS, BMS, RCA
•
Needs Based Budget
•
Planning Module
•
Work Accomplishments
•
Inventory
Pavement
Management
Pavement Condition Data –
Collection Length
• Total yearly collection: approx. 32,600 directional km
– Interstate: approx. 3,500 directional km (100% of IS
system)
– Primary: approx. 16,800 directional km (100% of PR
system)
– Secondary: approx. 12,300 directional miles (~20%
of SC system)
Data Analysis & Reporting
System Condition Ride Quality
GIS Maps
State of the Pavement Report Legislative Reports
Pavement Condition Data Collection and Analysis Program
Data Collection & QA
Control Sites
Production Data Collection
Independent Verification & Validation (QES)
Database Acceptance Contractor’s QC (RWG)
QA by VDOT
Unconstrained Needs Analysis
Define M&R Activities Extract Unit Costs from
TRNS*PORT
Unconstrained Needs Estimation Decision Matrices
Network Optimization
Set Performance Targets & Goals Develop Perf. Prediction Models
Performance based needs Optimization Allocation Distribution Pavement Condition Database Data loaded in PMS HPMS Reports
Data Collection
Equipment Capabilities
•Inventory from imagery •Location determined •Offset measured •Height and widthmeasured
•Sign code recorded •Condition assessment Assets •Image recognition software •Strobe-lit pavement video •Roughness •Texture •Rutting •Surface Distress •Ground Penetrating Radar Pavement •Inertial measurement unit •HPMS curve type •Long. Grade •Cross slope •Centerline mapping •Spatial referencing for GIS integration Geometry & Spatial •Single view •Panoramic view •1300 x 1030 pixel •1920 x 1080 (HDTV) •Direct-to-digital •Custom angles Photolog
Distance Measuring
Instrument (DMI)
• DMI utilizes a precision optical shaft encoder that
is mounted on the left rear driving wheel.
• The DMI records 2,000 pulses per revolution.
• Accuracy is ±0.02% of the linear distance traveled.
• Ensures accurate low
speed roughness
measurements down to 20 km/h (12.5 mph).
Pavement Images
• Rear downward facing cameras
• Continuous pavement images of full lane width
• Renders pavement distresses down to 2mm (0.08
Laser Rut Measuring System
• Pair of rear mounted INOLasers
• Measure full transverse profile of the road surface to over 1,200 points
• Transverse profile is
evaluated to determine the depths of ruts
International Roughness
Index (IRI)
• Laser SDP System • 16 kHz laser in each wheelpath • Measures continuous longitudinal profile of the roadwayHigh Definition Right of Way Images
• True High Definition 1920 x 1080 CCD Camera
• Wide angle High Definition images
• A single image every 21 feet (variable)
GPS Data
• Trimble System • Applanix® POSLV (Position and Orientation System) • Collected every station interval • Two antennas to give vehicle headingGPS Data
•Real Time GPS Data Collection to ensure proper collection and referencing.
•Inertial referencing system allows for fill in of missing GPS data.
Automated Distress Surveys –
Project Quality Process
• Daily/Weekly checks
• Control Sites
• Production data collection checks
• Independent Verification and Validation
Data Reporting
•Reporting of Condition Data
–Statistics:
• Percent/Lane-Miles Deficient • Condition Distributions
• Density of Key Distresses
–GIS Maps
• Distribution of Condition on Network (E, G, F, P, VP)
–Project and Treatment Selection
• Type and Distribution of Distress on Pavement 0.4% 0.1% 0.0% 0.7% 0.2% 0.0% 0.7% 0.3% 0.0% 1.0% 0.3% 0.0% 1.1% 0.6% 0.2% 0.5% 0.3% 0.1% 0.4% 0.1% 0.0% 0.9% 0.3% 0.1%
1/BR 2/SA 4/RI 5/HR 6/FR 7/CU 8/ST 9/NO
Interstate Asphalt Pavement - Transverse Cracking (% of total area )
Needs Analysis
• Two Types of Needs Analysis
– Unconstrained
• Provides a recommended treatment for entire network
• Based on network condition data and augmented using Age, Traffic and FWD Data
– Network Optimization
• Uses performance target to optimize treatment recommendations for budgeting and allocation
Maintenance Activity
Categories
• Do Nothing (DN) • Preventive Maintenance (PM) • Corrective Maintenance (CM) • Restorative Maintenance (RM) • Major Rehabilitation/Reconstruction (RC)Pavement Surface Distresses
Framework for Treatment
Selection
Preliminary Treatment Selection ··· Fatigue Cracking Transverse Cracking Rutting Patching Decision Trees Final Treatment Selection Traffic Level Structural Integrity Construction History Potholes + + Decision Matrices Preliminary Treatment Selection Decision Trees Traffic Level Structural Capacity Construction History + +Unconstrained Needs –
Decision Matrix
<0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" <0.5" >0.5" P0 DN DN DN CM CM RM DN DN DN CM CM RM CM CM CM CM CM RM P1 DN DN DN CM CM RM DN DN PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 DN DN DN CM CM RM DN DN DN CM CM RM CM CM CM CM CM RM P1 DN DN DN CM CM RM DN DN PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 CM CM CM CM CM RM CM CM CM CM CM RM CM CM CM CM CM RM P1 CM CM CM CM CM RM CM CM CM CM CM RM CM CM CM CM CM RM P2 RM RM RM RM RM RC RM RM RM RM RM RC RM RM RM RM RM RC P0 DN DN DN CM CM RM DN DN DN CM CM RM CM CM CM CM CM RM P1 DN DN DN CM CM RM DN DN PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 DN DN DN CM CM RM DN DN DN CM CM RM CM CM CM CM CM RM P1 DN DN DN CM CM RM PM PM PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM P1 RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM P2 RM RM RM RM RM RC RM RM RM RM RM RC RM RM RM RM RM RC P0 DN DN DN CM CM RM DN DN DN CM CM RM CM CM CM CM CM RM P1 DN DN DN CM CM RM DN DN PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 PM PM PM CM CM RM PM PM PM CM CM RM CM CM CM CM CM RM P1 PM PM PM CM CM RM PM PM PM CM CM RM CM CM CM CM CM RM P2 RM RM RM RM RM RC RM RM RM RM RM RC RM RM RM RM RM RC P0 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC P1 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC P2 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC P0 PM PM PM CM CM RM PM PM PM CM CM RM CM CM CM CM CM RM P1 PM PM PM CM CM RM PM PM PM CM CM RM CM CM CM CM CM RM P2 CM CM CM RM RM RC CM CM CM RM RM RC CM CM CM RM RM RC P0 CM CM CM CM RM RM CM CM CM CM RM RM CM CM CM CM RM RM P1 CM CM CM CM RM RM CM CM CM CM RM RM CM CM CM CM RM RM P2 RM RM RM RM RM RC RM RM RM RM RM RC RM RM RM RM RM RC P0 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC P1 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC P2 RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC RC > = 200 NS S VS S VS 75-199 NS S VS <10% >10% Rutting Sevrty Tr a n sve rse cr a cks p e r m ile 0-50 NS S VS 51-74 NS N <10% >10% N Rutting Freq. N <10% >10% Alligator Cracking Frequency Rare Alligator CrackingSeverity NS Severe Very Severe
<0.5" >0.5" <0.5" >0.5" <0 P0 DN DN DN CM C P1 DN DN DN CM C P2 CM CM CM RM RM P0 DN DN DN CM C P1 DN DN DN CM C P2 CM CM CM RM RM P0 CM CM CM CM C P1 CM CM CM CM C Rutting Sevrty 0-50 NS S S Rutting Freq. N <10% Alligator Cracking Frequency Alligator Cracking Severity NS
Performance Based Needs –
Optimization
• Collect and apply condition data to management sections
• Utilize pavement condition prediction models
• Use criteria for pavement maintenance activity selection
• Establish performance measures and targets
• Create planning scenarios and run optimizations
Performance Based Budgeting –
Optimization
• Single Year, Multi-Constraint Optimization
– Optimization of maintenance activities on
pavement management sections to achieve the objective function against multiple constraints for one year at a time
• Multi-Year, Multi-Constraint Optimization
– Optimization of maintenance strategies on a set of
pavement management sections to achieve the objective function against multiple constraints over multiple years
Multi-Constraint Optimization –
Objective Functions
• Two types of objective functions available
– Minimize Cost
– Maximize Benefit (or other condition indicator)
Trigger Limit P a v em ent C o ndi ti o n I n de x Age Benefit Existing Pavement Performance Min. Performance Predicted Pavement Performance
Optimization Results –
% Deficient
% of Pavem ent Netw ork in Deficient Condition by Lane Mile
15.0% 17.0% 19.0% 21.0% 23.0% 25.0% 0 1 2 3 4 5 6 7 8 9 10 FY % D e fi ci en t
Optimization Results –
% Needing Reconstruction
% of Pavem ent Netw ork Needing Reconstruction by Lane Mile
0.0% 5.0% 10.0% 15.0% 20.0% 0 1 2 3 4 5 6 7 8 9 10 FY % Ne e d in g RC
Optimization Results –
Average LM-Weighted CCI
Pavem ent Netw ork Average CCI by Lane Mile
70.0 72.0 74.0 76.0 78.0 80.0 0 1 2 3 4 5 6 7 8 9 10 FY Av e ra g e CCI
Bridge
Bridge Management
• VDOT maintains 19,356 structures, including 11,930 bridges and 7,426 large culverts.
• Structures receive routine inspections. The inspection quantifies the condition of the structure and provides a basis for
determining asset needs.
• AASHTO Pontis® bridge management system
software is used to process inspection data and perform network-level analysis for needs based budgeting.
Data Collection
• Federal regulations mandate the inventory and inspection of structures and the annual reporting of data to the National Bridge
Inventory (NBI).
• Each structure is inspected at regular
intervals by qualified Bridge Safety Inspectors in accordance with the requirements of the
National Bridge Inspection Standards (NBIS).
• Typically, a structure is inspected once every two years.
Determining Needs
• Pontis application software is used to analyze the needs of the bridge and large culvert
assets.
• A program simulation using a predefined
funding scenario is modeled to determine the unconstrained, unmet, preservation needs of structures.
• In general, Pontis recommends the most
beneficial action/treatment based on level of funding, element condition, probabilistic
deterioration trends and the cost of action/treatment.
RCA
•
Stands for “Random Condition
Assessment”
•
Establishes a statewide inventory and
condition assessment by extrapolation
of randomly selected and surveyed
samples
•
Does not include mainline pavements
and bridges
•
RCA assets constitute approximately
15% of total unconstrained needs
Assets Included in the
RCA Survey
• Pipes and Small Culverts
• Guardrails • Guardrail Terminals • Traffic Signs • Pavement Markings • Paved Ditches • Unpaved Ditches • Unpaved Shoulders
Planning - Inputs
Parameters Condition and Inventory
•Ordinary Maintenance •Corrective •Replacement •Rehabilitation
•Pavement – actual quantities •RCA Assets – sampled
quantities Planning Module Deterioration Rates Extrapolation Factors Regional Cost Factors Inflation Factor Total Asset Quantities Asset Repair Quantities Asset Repair Costs
Planning - Outputs
High Level Maintenance Plan (1 – 6 years)
Including…
• Total Asset Quantities •Starting Repair Quantities • Annual Work Quantities • Annual Work Costs
By... • District • Roadway System • Asset • Repair Group Planning Module
Planning Module - Output
Planning Module
In terms of the NBB process, the Planning Module produces…
•Total Unmet Needs
• Cost to Maintain Current Condition
Determining
Unconstrained Needs
• Modeled Assets – flexible and rigid pavements,
ditches, cross pipes, unpaved shoulders, guardrail, end terminals, traffic signs, paved shoulders,
non-hard surface roads, bridges and large culverts, traffic signals, overhead signs, smart traffic centers,
tunnels, ferries, rest areas, moveable bridges
• Non-Modeled Assets – roadside assets, retaining
walls, drainage, traffic devices, facilities
• Cost Centers and Programs – snow prep and
Asset Management Maturity
Scale – AASHTO 2011
Asset management strategies, processes, and tools support decisions and are regularly evaluated and improved.
Best Practice
Asset management strategies, processes, and tools provide information to establish agency expectations and accountability.
Proficient
Processes and tools developed. There is a shared understanding and motivation that results in
coordination of activities.
Structured
Recognition of the need for more systematic
processes and data collection activities. Efforts at this level typically rely on one or more champions.
Awakening
No systematic processes in place and little motivation to improve existing processes.
Initial
General Description TAM Maturity Scale
