NMDOT Pavement Management Uses in Meeting Federal Requirements and Project Selection

Transcription

1 NMDOT Pavement Management Uses in Meeting Federal Requirements and Project Selection April 24 and 26, 2018 Shawn Romero, EI Jeff Mann, PE Pavement Management And Design Bureau

2 Overview This Is What We are Talking About Generalized Pavement Condition Curve 2

3 Overview Introduction to and History of Pavement Management (PMS db) (JSM) What, When, How of NMDOT PMS db (JSM) PMS db Data Collection Procedures (SR) PMS db Data Collection QC/QA Procedures (SR) PMS db (SR) Projections, Scenarios, Budget, Project Selection 23 CFR 490 Requirements and Discussion Points (JSM) 3

4 Introduction to and History of Pavement Management Systems and Database (PMS db) Who Knows When it Started AASHO Road Test Ottawa, Illinois Pavement Serviceability Index (PSI) was born Concept that RIDE COMFORT along w Safety were the performance objections of ALL PAVEMENTS 1970s Several Papers on Pavement Management 1977 First Textbook Pavement Management Systems 4

5 Introduction to and History of Pavement Management Systems and Database (PMS db) 1986 (1993) AASHTO Guide for Design of Pavement Structures Chapter 2 Pertains to Network Level and Project Level Determination 23 CFR Part 626 (Non Regulatory) Recommends that Pavement Design shall be used in conjunction with performance and cost data from PMS db MAP 21, FAST Act, 23 CFR 490 Transportation Asset Mgmt Plan (TAMP) Require Minimum Standards for Operating PMS db for Interstates and NHS We will discuss later 5

6 Introduction to and History of Pavement Management Systems and Database (PMS db) What is a PMS db? From 2012, Second Ed Pavement Management Guide provides a systematic approach to management a pavement network that enables agencies (NMDOT) to evaluate the consequences associated with various investment decisions (THINK BUDGETS) and to determine the most cost-effective use of available funds (THINK PERFORMANCE) Network Level Data ie Performance of our Roadway System Reporting FHWA HPMS, LFC, 23 CFR 490 Project Level Data Managed Section Data of Distress (2 Mile Sections) NMDOT has ALL distress data for every 1/10 mile of across our network 6

7 An Effective Pavement Management System db Should Assess Current and Future Pavement Conditions Network Level Considerations and Analyze Performance Curves and Modeling Estimate Funding Needs to Achieve a Desired Condition Level Budgeting State of Good Repair Identify Preservation, Rehabilitation and Reconstruction Projects that Optimize Funding Project Level Analyses 7

8 An Effective Pavement Management System db Should con t Illustrate Consequences of Funding Levels on Condition LFC, FHWA, TAMP Reporting Performing Scenarios Justify and Defend Funding Levels Compared to Performance Reporting Methods 8

9 Discussion on Network Level Analyses vs Project Level Analyses Network Level Analyses Consider the Pavement Distress Condition of All Our Roads Used For Statewide Budgeting Used for Performance Forecasting Used for Reporting for Legislative Finance Committee on NM Performance Measures Used for Transportation Asset Mgmt to meet FHWA Requirements Composite Index Typically Used Used for District Budgeting 9

10 Discussion on Network Level Analyses vs Project Level Analyses Project Level Analyses 2 Mile Sections Consider Prevalent Distress and Suggest Recommendation Based on Decision Trees and Performance Curves and Cost:Benefit Simply if Roadway has this types of distress or is this age, then X recommendation Supplement w coring, field exploration, GPR, FWD PavementME or MEPDG Input Data Calibration of Pavement Distress Models Materials Database Traffic Database 10

11 Basics of Pavement Management Systems and Database (PMS db) Inventory What Is Important? Pavement Condition Data Roadway Segments, MP Linear Referencing System (LRS) Functional Classification Pavement Section, Type Shoulder Information Number of Lanes Construction History (1,900 Records) Integration with MMS Traffic Data and WIM Data Materials Related Data Cost Data 11

12 Pavement Condition Assessment Types of Pavement Condition Data Collected Distresses (FHWA LTPP Guide, 2014) Structural Capacity FWD? RWD? Traffic Speed Deflectomer? Surface Characteristics Friction? Noise? Techniques for Data Collection Manual Semi-Manual (NDT??) Fully Automatic NMDOT Since 2013 Moved to Fully Automatic 12

13 Pavement Condition Assessment Applicable AASHTO Standards R48: Standard Practice for Determining Rut Depth in Pavements R36: Standard Practice for Evaluating Faulting of Concrete Pavements R55: Standard Practice of Quantifying Cracks in Asphalt Pavement Surface R43: Standard Practice for Quantifying Roughness of Pavements (IRI) 13

14 NMDOT PMS db Distresses Based on Long Term Pavement Performance LTPP FHWA Guidance NMDOT Measures and Determines the Severity and Extent of Following Distresses Raveling and Weathering Bleeding Transverse Cracking Alligator Cracks Edge Cracks Longitudinal Cracks Patching Block Cracking IRI and Rutting and concrete pavement distress too 14

15 Developing Pavement Condition Indices What are Pavement Condition Indices? Typically a numerical index between 0 to 100 which is used to indicate condition of pavement. NMDOT use PCR (Pavement Condition Rating) Composite Index Subcategories Composite Index Individual Index Composite Index PCI, PCR, PSI Individual Index NMDOT uses Structural Index, Environmental, Safety Index, Roughness Index Evaluates Distress for Each Individual Index Used for Decision Trees 15

16 NMDOT PMS db History of Implementation 1990 s to 2006 (Fuzzy) 2006 Districts Provided Assistance on Pavement Distress Data Collection NMSU and UNM Provided Manual Data Collection PMS db Begins Steering Committee Formed w District and General Office Representation and KEI Engineering Hired to Help w Configuration Summer 2013 Executive Decision to move to Automated Distress Data Collection Methods Requiring New Configuration of Agile PMS db Moved to Automated, Mandli Data Collection Reconfiguration 2018 Fugro Reconfiguration Planned for Performance Curves based on Construction History and Maintenance History data 16

17 The NMDOT Pavement Management System db Can Assess Current and Future Pavement Conditions Network Level Considerations and Analyze Performance Curves and Modeling Estimate Funding Needs to Achieve a Desired Condition Level Budgeting State of Good Repair Identify Preservation, Rehabilitation and Reconstruction Projects that Optimize Funding Project Level Analyses Illustrate Consequences of Funding Levels on Condition LFC, FHWA, TAMP Reporting Performing Scenarios Justify and Defend Funding Levels Compared to Performance Reporting Methods 17

18 PMS Data Collection Procedures What type of distress are being collected How is the data being collected What control procedures are in place Why are we collecting this information How is this data being used 18

19 Data History Prior to automated collection NMDOT would collect IRI in house and contract a University to manual/visual survey and collect distress. Data definitions and some practices developed during manual survey were carried over to automated data collection 19

20 Data History New Mexico s pavement distress definitions and collection methods were derived from FHWA s Distress Identification and HPMS Manual along with data collection practices carried on from district distress field survey 20

21 Define Pavement Distress Alligator Cracks: Pattern of interconnected cracks resembling chicken wire or alligator skin. Longitudinal cracks in the wheel path are rated as Low severity alligator cracking. Severities 2 and 3 must have at least 3 cells. Edge Cracks: Cracks that lie within 1 foot of the edge line. Does NOT apply in roads withcurb andgutterinstallations. 1. Low: Hairline, disconnected cracks, 1/8-inch wide or less, less than 3 cells. No spalls.and/or a longitudinal crack,anyseverity, in the wheelpath. 2. Medium: Fully developed cracks greater than 1/8-inch wide. Three or more cells. Lightly spalled. 3. High: Severelyspalled, cells rock, and may pump. 1. Low: Less than ¼-inch wide. No spalls. 2. Med: Greater than ¼-inch wide. Some spalling may be present, but pavement is still intact. 3. High: Severelyspalled. Piecesof pavementhave broken off the edge of the roadway. PACE OFF the cumulative lengths of EACH severity present. Record lengths (in paces). Mark location of occurrence in field form: 1 or 2 wheel paths. 1. Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more. Longitudinal Cracks: ANY longitudinal crack NOT in the wheel path, but NOT within 1 of the edge line. 1. Low: Unsealed, mean width of less than ¼-inch. OR sealed with sealant in good condition, any width. 2. Medium: Any crack with average width greater than ¼- inch and less than ¾ inch. May have adjacent Low severity randomcracksand some spalling. 3. High: Any crack wider than ¾ inch, may have adjacent moderate to high randomcracking and spalling. 1. Low: 1% to 30% of sample section. 2. Medium: 31% to 60% of sample section. 3. High: 61% or more of sample section. Patching: Any new pavement placed into the pavement section. Extent is rated as percent of the test section affected. 1. Low: Patch is in good condition. 2. Medium: Somewhat deteriorated, has Low to Medium severities of any distress present. 3. High: Needs replacement. High Severity of any distress, gaps are present between the pavement and the patch. 1. Low: 1% to 30% 2. Med: 31% to 60% 3. High: 61% or more

22 Automated Data Collection 2013 The Department contracted with Mandli Communications to collect fully automated distress data, a 4 year contract a new vendor (Fugro) was selected to collect distress data for the next for years. 22

23 Data Collection 15,000 Lane Miles each year, including: 100% NHS routes 50% Non-NHS routes Pavement Distress Indices: IRI Rutting Faulting Raveling Bleeding Patching Cracking Corner Break Joint Count Joint Seal Damage Joint Crack Spalling Photologs Lidar and Assets Collected in

24 How is the Data Collected 24

25 Data Collection Rutting Laser Crack Measurement System (LCMS) Collect Left, Right and Average Rut Depth (in) Faulting Laser Crack Measurement System (LCMS) Average (in) (27 Sev low +1 Sev med )/34 joint count )(100)= 82%= High Extent (3) Severity Extent Severity 1. Low: Faulted joints or cracks which average 1/16-inch or less. 2. Med: Faulted joints or cracks which average more than 1/16-inch; but less than 1/4-inch. 3. High: Faulted joints or cracks which average 1/4-inch or more. Extent 1. Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more. 25

26 Cracking Data Collection Laser Crack Measurement System (LCMS) Collect: -HPMS Percent Length Longitudinal Block Fatigue - Corner -Edge Longitudinal Cracks: ANY longitudinal crack NOT in the wheel path, but NOT within 1 of the pavement white line. Severity 1. Low: Unsealed, mean width of less than ¼-inch. OR sealed with sealant in good condition, any width. 2. Medium: Any crack with average width greater than ¼-inch and less than ¾-inch. May have adjacent Low severity random cracks and some spalling. 3. High: Any crack wider than ¾-inch, may have adjacent moderate to high random cracking and spalling Extent 1. Low: 1% to 30% of test section. 2. Med: 31% to 60% of test section. 3. High: 61% of test section, or more. 26

27 Data Collection HPMS Cracking 30 Wheel path to 39 (2017 Collection) AASHTO Designation: PP Release: Group 1(April 2016) 27

28 Data Collection International Roughness Index (IRI) Dynatest MK-IV Road Surface Profiler Collect Left, Right and Average IRI (In/Mile) US-60 Eastbound MP IRI Average Left Right Poor Inches/Mile Fair Good Milepoint 28

29 Example: Downward facing view of roadway 29

30 Example: Lidar 30

31 Data Quality Assurance/Control Contractor Quality Control (QC) Pre-Deployment Vehicle Configuration Collection vehicle to meet criteria specified by NMDOT Laser Crack Measurement System (LCMS) configuration Positional Orientation System (POS) configuration Camera Setup System Certification Ten 0.1 mile runs of IRI data have less than 5% standard deviation from the mean Internally created procedure taking components from AASHTO R runs of LCMS data have a minimum repeatability of 92% compared to profile created with a SurPRO (ProVAL Certification Module) Compares well to historical data from the same course 31

32 Data Quality Assurance/Control Contractor Quality Control (QC) Daily Checks Bounce test values do not exceed 8 in./mile (elements from AASHTO R-57-14) LCMS Static Validation performed on a weekly basis LCMS height detection is comparable to previous days reading Ambient temperature is within system operating ranges of (>32ºF <104ºF) Imagery is in focus, color is appropriate, and is of acceptable quality Monitor the GPS satellite coverage, along with reported accuracies Monitor the photolog and downward imagery for quality and lane placement Use a mapping program to determine completeness of collection 32

33 Data Quality Assurance/Control Contractor Quality Control (QC) Validation Sites Validation courses have been strategically located to aid in the efficiency of collection, and confirm the systems are functioning in multiple scenarios Analyze and validated by third party independent firm Report right wheel path standard deviation from data collection vehicles initial validations compared to final, for each deployed vehicle Report left wheel path standard deviation from data collection vehicles initial validations compared to final, for each deployed vehicle Report right rutting average variance from data collection vehicles initial validations compared to final Report left rutting average variance from data collection vehicles initial validations compared to final 33

34 Data Quality Assurance/Control Contractor Quality Control (QC) Data Reduction Data reduction team is properly trained and tested on manual distress classifications and rating rules prior to rating of production data Check LCMS data for null rutting values, invalid rutting values, outside acceptable temperatures Distress production data is reviewed to assure rating understanding remains consistent through the course of the project Data Delivery Roadway conditions outliers are performed on aggregated 1 mile segments Max and Min Values IRI Max and Min Values (30>IRI>400), differ by more than 50 in/mile. Rutting Max Values greater than 0.35 in, differ by more than.25in Faulting Max Values greater than 1in. Roadway Geometric Outliers Absolute Values exceeding 8% Absolute grade values greater than 12% Absolute Curve values greater than 100 degrees No more than 10 consecutive fixed segments will be missing data (500ft) 34

35 Data Quality Assurance/Control Department Quality Assurance (QA) New Mexico DOT receives data from the Data Collection Contractor on a monthly basis and conducts a review up to 10% of the submitted data and reports any inconsistences to the Data Collection Contractor s Project Manager for action (i.e., correction, re-collection). 35

36 Data Quality Assurance/Control Data Delivery Checks Total network miles (excludes areas closed to construction) Delivered data accurately populated with description information (system, route, direction, and begin and end latitude/longitude Photo Images are clear and aligned For 10 th mile segments Raveling values must add up to 528 feet. Maximum bleeding can be 528 feet. Maximum fatigue (alligator) cracking can be 6336 square feet. Maximum longitudinal cracking can be 1584 linear feet. Maximum edge cracking can be 528 feet. Maximum block cracking can be 6336 square feet. 36

37 Data Quality Assurance/Control Department Quality Assurance (QA) Contractor resolution 37

38 National Performance Management Measures; Assessing Pavement Condition for the National Highway Performance Program and Bridge Condition for the National Highway Performance Program 23 CFR Part Other requirements. (2) Not later than 1 year after the effective date of this regulation, State DOTs shall submit their Data Quality Management Program to FHWA for approval. 38

39 Practical Guide for Quality Management of Pavement Condition Data Collection This guide outlines a process for systematically implementing QM practices throughout the data collection effort. It describes the roles and responsibilities for successful QM of the data and presents the practices currently in use by transportation agencies. 39

40 Revising Data Quality Management Plan for FHWA s Acceptance Linda Pierce 23 CFR Part 490 FHWA Comments NMDOT s DQMP Draft Current QA Practices Data Quality Management Plan 40

41 Revising Data Quality Management Plan for FHWA s Acceptance Other requirements. (1) In a Data Quality Management Programs, State DOTs shall include, at a minimum, methods and processes for: (i) Data collection equipment calibration and certification; (ii) Certification process for persons performing manual data collection; (iii) Data quality control measures to be conducted before data collection begins and periodically during the data collection program; (iv) Data sampling, review and checking processes; and (v) Error resolution procedures and data acceptance criteria. 41

42 Additions to DQMP Training Certifications Vendor Dispute Resolutions Ground Truth 42

43 Moving forward with DQMP Supporting document to the DQMP that includes Pavement definitions and descriptions Field collection procedures Guidelines to rate sections based on digital images Certification test 43

44 What we are Doing With the Data 1. Providing a network view of pavement conditions Reporting conditions to legislators and federal government 2. Assisting in determining future projects 3. Supports funding request with data and modeled predictions

45 Federal Reporting 23 CFR Part 490 National Performance Measures Currently do not have measures set up in the pavement management system Calculation of overall condition pavement condition with requirements of SS (b) Rating JCP CRCP Flexible Flexible Rigid All Pavements Cracking (%) Cracking (%) Cracking (%) Rutting (Inches) Faulting (Inches) IRI (in/mile) Rating Good 0 < 5 0 < 5 0 < < < Good Fair Fair Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor TOTAL TOTAL HPMS Cracking Percent Overall Pavement Condtion Measure GOOD FAIR POOR Route Lane Begin Mile End Mile Length Lane Miles Average IRI Rutting Measure BL-11-P All FAIR 122. FAIR 0.14 GOOD FAIR 0.2 BL-11-P All GOOD 226. POOR 0.13 GOOD FAIR 0.2 BL-11-P All GOOD 132. FAIR 0.10 GOOD FAIR 0.2 BL-11-P All GOOD 86. GOOD 0.11 GOOD GOOD 0.4 BL-11-P All GOOD 64. GOOD 0.10 GOOD GOOD 0.4 BL-11-P All GOOD 69. GOOD 0.12 GOOD GOOD 0.4 BL-11-P All GOOD 63. GOOD 0.13 GOOD GOOD 0.4 BL-11-P All GOOD 51. GOOD 0.15 GOOD GOOD 0.4 BL-11-P All GOOD 52. GOOD 0.14 GOOD GOOD 0.4 BL-12-P All POOR 95. FAIR 0.26 FAIR FAIR 0.2 BL-12-P All POOR 108. FAIR 0.17 GOOD FAIR 0.2 BL-12-P All POOR 89. GOOD 0.16 GOOD FAIR 0.2 BL-12-P All POOR 129. FAIR 0.20 FAIR FAIR 0.2 BL-12-P All POOR 118. FAIR 0.22 FAIR FAIR 0.2 BL-12-P All POOR 97. FAIR 0.21 FAIR FAIR 0.2 BL-12-P All FAIR 243. POOR 0.20 FAIR FAIR 0.2 BL-12-P All FAIR 133. FAIR 0.19 GOOD FAIR 0.2 BL-12-P All POOR 232. POOR 0.30 FAIR POOR 0.2 BL-12-P All FAIR 163. FAIR 0.24 FAIR FAIR

46 Federal Reporting 46

47 State Reporting Pavement Condition Rating Pavement Condition Rating Lane Miles Good (PCR>=65.5) Fair (65.5<PCR<=45.5) Poor (PCR<45.5) Lane Miles Good (PCR>=65.5) Fair (65.5<PCR<=45.5) Poor (PCR<45.5) Lane Miles Good (PCR>=65.5) Fair (65.5<PCR<=45.5) Poor (PCR<45.5) Systemwide 31,007 9,388 30% 15,765 51% 5,854 19% 30,965 10,414 34% 14,920 48% 5,631 18% 30,770 10,147 33% 15,452 50% 5,171 17% NHS 12,050 5,041 42% 5,534 46% 1,356 11% 12,050 5,641 47% 4,987 41% 1,280 11% 12,050 5,605 47% 5,278 44% 1,021 8% Non-NHS 19,075 4,347 23% 10,231 54% 4,497 24% 19,057 4,773 25% 9,933 52% 4,352 23% 18,865 4,543 24% 10,173 54% 4,149 22% Interstate 4,108 2,297 56% 1,515 37% 296 7% 4,105 2,344 57% 1,452 35% 310 8% 4,105 2,393 58% 1,490 36% 223 5% Non-Interstate 26,899 7,092 26% 14,250 53% 5,557 21% 26,860 8,070 30% 13,467 50% 5,322 20% 26,664 7,755 29% 13,961 52% 4,948 19% Non-NHS Non Interstate 19,075 4,347 23% 10,231 54% 4,497 24% 19,057 4,773 25% 9,933 52% 4,352 23% 18,865 4,543 24% 10,173 54% 4,149 22%

48 Pavement Management System A Pavement Management System (PMS) is designed to provide objective information and useful data for analysis so that road managers can make more consistent, cost-effective, and defensible decisions related to the preservation of a pavement network. While a PMS cannot make final decisions, it can provide the basis for an informed understanding of the possible consequences of alternative decisions. 48

49 Data Standardization Pavement Management System Levels the playing field by converting each distress value to a scale Overall Condition Index (OCI) is calculated by combining distress indices Does not include Roughness IRI Worst No distress

50 Pavement Management System Unconstrained Needs Route Begin Mile End Mile Maintenance District Mainline Treatment Mainline Treatment Costating (PCR) Average IRI Rutting Measureng Percent I-25-M ALBUQUERQUE F4 - Preservation (Major) $390, I-25-M SANTA FE F4 - Preservation (Major) $390, I-25-M LAS VEGAS F4 - Preservation (Major) $460, I-25-M LAS VEGAS F4 - Preservation (Major) $460, NM-478-M DEMING F3 - Preservation (Minor) $8, BL-36-P LAS VEGAS F3 - Preservation (Minor) $9, NM-37-P ROSWELL F3 - Preservation (Minor) $9, NM-38-P LAS VEGAS F3 - Preservation (Minor) $10, US-54-P LAS VEGAS F3 - Preservation (Minor) $5, I-25-P ALBUQUERQUE F3 - Preservation (Minor) $10, I-25-M ALBUQUERQUE F3 - Preservation (Minor) $10, NM-314-P ALBUQUERQUE F3 - Preservation (Minor) $10, NM-14-P SANTA FE F3 - Preservation (Minor) $20, NM-14-M SANTA FE F3 - Preservation (Minor) $20, BL-13-M ALBUQUERQUE F3 - Preservation (Minor) $10, US-60-P ROSWELL F3 - Preservation (Minor) $11, NM-268-P ROSWELL F3 - Preservation (Minor) $11,

51 Performance Prediction AgileAssets Pavement System Collected pavement distress data is stored in the pavement management system Individual Distress Indices are combined to structural, Environmental, Safety, Roughness and Overall Condition Index

52 Performance Prediction Treatment and Condition Improvement Rules

53 Performance Prediction Pavement Management System Performance Models Deterioration Curve Piecewise linear function

54 Performance Prediction Decisions are driven by cost benefit Exclusion Years are used

55 PMS Logic PMS makes pavement treatment selections based on decision trees Monitor Preventative Patch Preservation (Minor) Preservation (Major) Rehabilitation (Minor) Rehabilitation (Major) Reconstruction

56 PMS Scenarios Multi Constraint Optimization - $200M/Yr for 10 Years

57 PMS Scenarios - Reporting Multi Constraint Optimization OCI and treatment cost total Preservation Rehabilitation

58 Project Selection After a scenario is ran the PMS out puts a list of projects to be considered

59 Pavement Condition Reports Recommendations should consider: PCR Value Individual Distresses Field Exploration (Core and Core Hole evaluation) Ground Penetrating Radar (GPR) survey Field Evaluation

60 Reporting Pavement Condition FHWA guidelines in 23 CFR Part 490 set new thresholds for determining pavement conditions Cracking (%) Rutting (Inches) IRI (in/mile) Rating 0 < < Good Fair 10 < 0.40 < 170 < Poor

61 Pavement Condition Reports

62 Pavement Condition Reports

63 Pavement Condition Report Core evaluation influences pavement recommendations

64 Pavement Condition Report GPR evaluation influences pavement recommendations

65 Condition Report Provides 1. Data driven pavement location and treatment decisions, in lieu of funding based or worst first pavement recommendations 2. Summary of the roadway condition which include all topical distresses, core and bore information as well as GPR infomation 3. An ultimate scoping tool to make the best project decisions

66 Three main approaches to targetsetting using a PMS APPROACH (SCALE) KEYS TO GETTING IT RIGHT Program (network) level Confidence in relationship between program actions and reported performance Sufficient data to support relationship Responding to changes in treatments/policies Project (section) level Accurate reflection of investments Performance prediction models for sections Solving the aggregation problem 0.10-mile (reporting interval) level Applying performance prediction models and condition resets at the project level to 0.10-mile segments 66

67 23 CFR 490 To Establish National Performance Management Measures PAVEMENT, Safety, Bridge, Congestion, ect For Pavement Established May 20, 2017 MAP-21 and FAST ACT Require Transportation Asset Mgmt Plan (TAMP) 67

68 23 CFR 490 Summary: purpose of this rule Establish measures for State departments of transportation to use to carry out the National Highway Performance Program (NHPP) Assess the condition of Pavements on the National Highway System (NHS) pavements on the Interstate System Ensure that investments of Federal-aid funds are directed to support progress toward the achievement of PERFORMANCE TARGETS established in a State s asset management plan for NHS. Establishes regulations that address measures, targets, and reporting 68

69 23 CFR Establishment of Performance Targets THIS IS WHY WE ARE HERE State DOT and MPO shall establish performance targets for all measures for condition of pavements on Interstate System and Non-Interstate NHS State DOT shall establish target 1 year from effective date of rule (by May 20, 2018) State DOT shall COORDINATE with relevant MPO on selection of targets 69

70 23 CFR Establishment of Performance Targets THIS IS WHY WE ARE HERE State DOT shall provide Baseline Performance Period Report where performance period is 4 years Require a 2-year Target Require a 4-year Target Reporting State DOT shall report Baseline, 2- and 4-year targets and the basis for established targets (PMS and Budget Allocation) State DOT shall provide relevant MPO targets to FHWA 70

71 23 CFR Establishment of Performance Targets THIS IS WHY WE ARE HERE MPO shall establish targets for each performance measure MPO shall establish targets no later than 180 days after State DOT target established MPO shall establish 4-year target 71

72 23 CFR Establishment of Performance Targets CRITICAL POINT MPO has option to agreeing to plan and program projects so that they contribute toward the accomplishment of relevant State DOT target OR Committing to a quantifiable target for that performance measure for their metro planning area 72

73 23 CFR Pavement Performance (Distress) Metrics Based on (current) HPMS Field Manual method of collection and calculation 1/10 Mile Data Asphalt Pavements IRI (PSR), Rutting, % Cracking Jointed Concrete Pavements IRI (PSR), Faulting, % Cracking Continuously Reinforced Concrete Pavements IRI (PSR), % Cracking NMDOT Pavement Data Collection Cycle Yearly NHS and Interstate Every Other Year Non-NHS (full collection every 2 years) Previous Vendor New Contract

74 23 CFR Calculation of Pavement Performance (Distress) Measures Pavement Measures (IRI, % Cracking, ect) from Shall Be Calculated used by State DOT and MPO to carry out pavement condition related requirements Performance Measures is good, fair and poor based on following rating (criteria) More than 2 Performance Measures are Poor Roadway Segment is Classified as poor Rating JCP CRCP Flexible Flexible Rigid All Pavements Cracking (%) Cracking (%) Cracking (%) Rutting (Inches) Faulting (Inches) IRI (in/mile) Rating Good 0 < 5 0 < 5 0 < < < Good Fair Fair Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor 74

75 23 CFR Part th Mile Thresholds Rating JCP CRCP Flexible Flexible Rigid All Pavements Cracking (%) Cracking (%) Cracking (%) Rutting (Inches) Faulting (Inches) IRI (in/mile) Rating Good 0 < 5 0 < 5 0 < < < Good Fair Fair Poor 15 < 10 < 20 < 0.40 < 0.15 < 170 < Poor Overall Condition: Good: All three ratings are Good Poor: Two or more ratings are Poor Fair: Does not meet Good or Poor Condition Overall Pavement Condition Year Route Lane Begin Mile End Mile Length Lane Miles HPMS Cracking Percent Average IRI Rutting Measure 2015 BL-11-P All FAIR 122. FAIR 0.14 GOOD FAIR 2015 BL-11-P All GOOD 226. POOR 0.13 GOOD FAIR 2015 BL-11-P All GOOD 132. FAIR 0.10 GOOD FAIR 2015 BL-11-P All GOOD 86. GOOD 0.11 GOOD GOOD 2015 BL-11-P All GOOD 64. GOOD 0.10 GOOD GOOD 2015 BL-11-P All GOOD 69. GOOD 0.12 GOOD GOOD 2015 BL-11-P All GOOD 63. GOOD 0.13 GOOD GOOD 2015 BL-11-P All GOOD 51. GOOD 0.15 GOOD GOOD 2015 BL-11-P All GOOD 52. GOOD 0.14 GOOD GOOD 2015 BL-12-P All POOR 95. FAIR 0.26 FAIR FAIR 2015 BL-12-P All POOR 108. FAIR 0.17 GOOD FAIR 2015 BL-12-P All POOR 89. GOOD 0.16 GOOD FAIR 2015 BL-12-P All POOR 129. FAIR 0.20 FAIR FAIR 2015 BL-12-P All POOR 118. FAIR 0.22 FAIR FAIR 2015 BL-12-P All POOR 97. FAIR 0.21 FAIR FAIR 2015 BL-12-P All FAIR 243. POOR 0.20 FAIR FAIR 2015 BL-12-P All FAIR 133. FAIR 0.19 GOOD FAIR 2015 BL-12-P All POOR 232. POOR 0.30 FAIR POOR 2015 BL-12-P All FAIR 163. FAIR 0.24 FAIR FAIR 75

76 23 CFR Pavement Minimal Level of Condition (LOC) Minimal LOC of Interstate NHS percentage of lane-miles of Interstate System in Poor condition shall not exceed 5.0 percent NMDOT 2017 Current Condition of Interstate is <1% Poor 76

77 23 CFR Pavement Minimal Level of Condition Minimal LOC Non-Interstate NHS CFR Does NOT REQUIRE Minimum LOC for Non-Interstate NHS NMDOT 2017 Current Condition of Non-Interstate NHS is 5.9% Poor 77

78 NMDOT NHS Current and Projected Condition Rating Interstate 78

79 NMDOT NHS Current and Projected Condition Rating Non-Interstate NHS 79

80 NMDOT NHS Pavements and Bridges Federal 2 &4 Year Projected Targets Performance Measure 2 Year (2019) 4 Year (2021) Percentage of Bridges on the NHS in Good condition Percentage of Bridges on the NHS in Poor condition Percentage of Interstate pavement on the NHS in Good condition Percentage of Interstate pavement on the NHS in Poor condition Percentage of Non-Interstate pavement on the NHS in Good Condition Percentage of Non-Interstate pavement on the NHS in Poor Condition 36.0% 30.0% 3.3% 2.5% 57.3% 59.1% 4.5% 6.3% 35.6% 34.2% 9.0% 12.0% 80

81 Santa Fe MPO NHS Historical Data Interstate Non-Interstate 81

82 Mid Region MPO NHS Historical Data Interstate Non-Interstate 82

83 El Paso MPO NHS Historical Data Interstate Non-Interstate 83

84 Farmington MPO NHS Historical Data Interstate Non-Interstate 84

85 Farmington MPO NHS Historical Data 85

86 Mesilla Valley MPO NHS Historical Data Interstate Non-Interstate 86

87 Mesilla Valley MPO NHS Historical Data 87

88 Resources Practical Guide for Quality Management of Pavement Condition Data Collection DISTRESS IDENTIFICATION MANUAL for the Long- Term Pavement Performance Program /13092/13092.pdf Highway Performance Monitoring System Field Manual 23 CFR Part 490

89 Thank you, Jeffrey Mann Shawn Romero 89