Using Profile Data for Supporting Asset Management Decisions

Transcription

1 Using Profile Data for Supporting Asset Management Decisions Gerardo W. Flintsch Director, Center for Sustainable transportation Infrastructure Professor of Civil and Environmental Engineering Center for Sustainable Transportation Infrastructure

2 Outline o Asset Management decisions o How do we use profile data to support these decisions? o What is the level of detail and accuracy required? o Some potentially relevant questions o Final thoughts Center for Sustainable Transportation Infrastructure

3 Asset Management decisions & business processes Center for Sustainable Transportation Infrastructure

4 Asset Management o Systematic process of maintaining, upgrading, and operating physical assets cost-effectively, efficiently, and Engineering comprehensively. Economics Integration Businesslike Objectives

5 The Asset Management Process STRATEGIC ANALYSIS INFORMATION MANAGEMENT Goals & Policies System Performance Economic / Social & Environmental Budget Allocations INVENTORY DATABASE CONDITION USAGE MAINTENANCE STRATEGIES NETWORK-LEVEL ANALYSIS TOOLS CONDITION ASSESSMENT PERFORMANCE PREDICTION PRIORITIZATION / OPTIMIZATION PROGRAMMING PROJECT SELECTION PRODUCTS NETWORK-LEVEL REPORTS Performance Assessment Network Needs Facility Life-cycle Cost Optimized M&R Program Performance-based Budget FEEDBACK NEEDS ANALYSIS GRAPHICAL DISPLAYS PERFORMANCE MONITORING WORK PROGRAM EXECUTION PROJECT LEVEL ANALYSIS (Design) CONSTRUCTION DOCUMENTS

6 U.S. Map -21 National Goals Focus the Federal-aid program on the following national goals: 1. Safety 2. Infrastructure condition 3. Congestion reduction 4. System reliability 5. Freight movement and economic vitality 6. Environmental sustainability 7. Reduced project delivery delays Center Source: for Sustainable Transportation Infrastructure

7 The Asset Management Process STRATEGIC ANALYSIS INFORMATION MANAGEMENT Goals & Policies System Performance Economic / Social & Environmental Budget Allocations INVENTORY DATABASE CONDITION USAGE MAINTENANCE STRATEGIES NETWORK-LEVEL ANALYSIS TOOLS CONDITION ASSESSMENT PERFORMANCE PREDICTION PRIORITIZATION / OPTIMIZATION PROGRAMMING PROJECT SELECTION PRODUCTS NETWORK-LEVEL REPORTS Performance Assessment Network Needs Facility Life-cycle Cost Optimized M&R Program Performance-based Budget FEEDBACK NEEDS ANALYSIS GRAPHICAL DISPLAYS PERFORMANCE MONITORING WORK PROGRAM EXECUTION PROJECT LEVEL ANALYSIS (Design) CONSTRUCTION DOCUMENTS

8 How do we use profile data to support these decisions? Center for Sustainable Transportation Infrastructure

9 Infrastructure Condition/ Performance Indicators Pavements Service and User Perception Physical Condition Structural Integrity / Load-Carrying Capacity Safety and Sufficiency Environmental Impact Serviceability (PSI, IRI) Distress (PCI) Deflection (FWD) Friction (FN)/ Macrotexture Tire/Pav. Noise Rolling Resistance

10 Examples o Strategic level Performance monitoring o Network level Pavement management o Project level Smoothness Specification Research LTPP Center for Sustainable Transportation Infrastructure

11 Construction Acceptance o Smoothness for quality acceptance Incentives for superior smoothness Disincentives for roughness that exceeds targets Virginia DOT: IRI targets for Interstate and Non-Interstate pavements [applied to 0.01-mile (16 m) pay lots] o Use of Ride Spec turns back the clock by as much as 7 years No significant impact on HMA bid price Center for Sustainable Transportation Infrastructure (McGhee & Gillespie)

12 Virginia Smoothness Specification IRI (inches/mile) to 8 in/mi decrease Time in Service (years) Center for Sustainable Transportation Infrastructure w/o Spec Prov w/ Spec Prov "Terminal" IRI

13 Pavement Management o Smoothness has been a key parameter for supporting network-level decisions since the genesis of PMS What, When, Where USA: AZ and KS started collecting roughness in the early 70 s Developing counties: key input for the HDM model o Trigger preservation & rehabilitatiotn o Impact on user costs and environmental impacts Center for Sustainable Transportation Infrastructure

14 f1 VA: Average IRI by County (1997) Primary Highways Source: 1997 State of the Pavement Report

15 Bild 14 f1 start here Th flintsch;

16 Roughness Prediction for AZ DOT 160 I 10 WB, MP Roughness (Maysmeter Units) Threshold Roughness Value for Interstates ` Effect of M&R RSL Remaining Service Life Estimate 20 Preservation Treatment Year

17 System Performance Monitoring o Federal government in the US has used smoothness for assessing road performance for many years. FHWA (old) Roughness Objective: To increase the percentage of miles on the NHS that meet Owner- Agency managed pavement performance for acceptable ride quality to over 93 percent within 10 years IRI less than or equal to 170 inches/mile o Needs reliable data for aligning investments with desired performance HPMS HERS Center for Sustainable Transportation Infrastructure

18 Performance Measures Being Considered for MAP 21 ( 150(c)) PROGRAM National Highway Performance Program Highway Safety Improvement Program MEASURE CATEGORY Pavement Condition on the Interstates Pavement Condition on Non-Int. NHS Bridge Condition on NHS Performance of Interstate System Performance of Non-Interstate NHS Serious Injuries per VMT Fatalities per VMT Number of Serious Injuries Number of Fatalities CMAQ Program Traffic Congestion On-road mobile source emissions Freight Policy Freight Movement on the Interstate Source: T. Van, 11 th Infrastruture Management Research and Education Workshop, Washington, DC, Jan 2013

19 Condition of Principal Highways (2009) Interstate Pavement Smoothness (IRI) by State Highway Fatality Rates Bridge Deficiencies Source:

20 What is the level of detail and accuracy required? Center for Sustainable Transportation Infrastructure

21 Background: Pavement Surface Properties Consortium 1. Organize annual equipment rodeos + verification 2. Seasonal monitoring 3. Evaluation & development of new technologies 4. Evaluation of high-friction systems 5. CFME deployment & friction technology transfer 6. Outreach: Pavement Evaluation 2010 SURF 2012

22 Equipment Comparisons / Rodeos o Since 2007 o o o Profile, friction, texture Added Noise AASHTO R56 for certification Center for Sustainable Transportation Infrastructure

23 AASHTO R56 o Focused on profilers used for quality control and also applicable for network profilers o Uses cross correlation to evaluate: Repeatability (CC 92%) Ten runs Cross correlate with each other and average Accuracy (CC w/ reference 90%) Ten runs Cross correlate with reference and average ProVAL Software Center for Sustainable Transportation Infrastructure

24 Rodeo concept closely tied to US RPUG Center for Sustainable Transportation Infrastructure

25 We run ours at the Virginia Smart Road VTTI Road Bridge

26 Reference Profiler Comparison Cross-Correlation Left Right SECTION 1 JRCP SECTION 2 CRCP G&G SECTION 3 SMA/OGFC SECTION 4 SM SECTION 5 SM 9.5/ Can we use it smooth pavements?

27 IRI Comparison Section 4 SM Reference IRI (in/mi) LWP RWP IRI (in/mi) LWP RWP

28 Repeatability Section 4 SM % Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right GA MS SC1 SC2 AMES VTTI VA PENN Average Left: 93.7 % Average Right: 93.9 % 25 mph 45 mph

29 Reproducibility Section 4 SM 9.5 % % mph Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right GA MS SC1 SC2 AMES VTTI VA PENN Average Left: 79.6 % Surpro VT Surpro MS Average Right: 78.4 % mph Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right GA MS SC1 SC2 AMES VTTI VA PENN Average Left: 80.0% Surpro VT Surpro MS Average Right: 78.3%

30 Some potentially relevant questions Center for Sustainable Transportation Infrastructure

31 Do we need a hierarchical specification for profilers?

32 Information Quality Levels HIGH LEVEL DATA Strategic Level IQL-5 Performance System Performance Monitoring Network Level IQL-3 IQL-4 Structure Condition Planning and Performance Evaluation Program Analysis or Detailed Planning Project Level IQL-1 IQL-2 LOW LEVEL DATA Project Level or Detailed Programming Project Detail or Research

33 Can we use probe (or regular) vehicles for road infrastructure health monitoring? At least for supporting high-end strategic- and network-level decisions?

34 Pavement Assessment and Management Applications Enabled by the Connected Vehicles Environment Proof-of-Concept Objective: To use data collected from probe vehicles to extract information that could be used to remotely and continuously determine road infrastructure health

35 Comparison

36 Is IRI the most appropriate way of summarizing the profile data?

37 How significant is the impact of smoothness on vehicle operation costs and GHG emissions? Can profile data help more sustainable network-level pavement management decisions? Center for Sustainable Transportation Infrastructure

38 Incorporating pavement LCA usephase into pavement management National Sustainable Pavement Consortium Energy Energy Energy Energy Cost (Tens of Thousands of Dollars) Max 2.8 x Min 2.4 Min 2.2 Energy Consumption (MJ) Condition, Condition Max Max 1.6 Condition Condition th Percentile 5th Percentile 95th Percentile 100 Min Cost, Cost 90 Max 80 Cost Cost 70 Condition Min Min 60

39 Final Thoughts Center for Sustainable Transportation Infrastructure

40 Final Thoughts o Profile data is a key asset management input user perception & level of service o It is used (and needed) for supporting business processes at various management levels o We may not need the same degree of detail and accuracy for all levels Center for Sustainable Transportation Infrastructure

41 Blacksburg, VA