Lowering Pavement Evaluation Costs Using Big Data

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SOLVING PROBLEMS OF GLOBAL IMPORTANCE www.ara.

1 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Lowering Pavement Evaluation Costs Using Big Data Bill Buttlar UIUC Bill Vavrik ARA T.H.E Applied Research Associates, Inc. 1

2 SOLVING PROBLEMS OF GLOBAL IMPORTANCE The evolution of pavement data collection Manual Vehicle based data collection Film Digital Current 3D Systems PaveVision Smart Phone Smart Roads Applied Research Associates, Inc. 2

3 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Pavement Evaluation Data Collection Continues to Evolve Applied Research Associates, Inc. 3

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5 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Pavement Evaluation Data Collection Continues to Evolve Applied Research Associates, Inc. 5

6 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Pavement Evaluation Data Collection Continues to Evolve Applied Research Associates, Inc. 6

7 SOLVING PROBLEMS OF GLOBAL IMPORTANCE The Current State of the Art is 3D Applied Research Associates, Inc. 7

8 SOLVING PROBLEMS OF GLOBAL IMPORTANCE The analysis potential increases Range 3D Intensity Applied Research Associates, Inc. 8

9 SOLVING PROBLEMS OF GLOBAL IMPORTANCE The next generation is now here Applied Research Associates, Inc. 9

10 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Advanced Computer Vision Is Changing Data Analysis Applied Research Associates, Inc. 10

11 SOLVING PROBLEMS OF GLOBAL IMPORTANCE PaveVision is low cost with the ability for automated data analysis Applied Research Associates, Inc. 11

12 SOLVING PROBLEMS OF GLOBAL IMPORTANCE And it will continue to evolve Applied Research Associates, Inc. 12

13 Road User s Opinion of Roughness 13

94% are surfaced with asphalt 32% of major roads are in poor condition

14 Road and Highway Conditions Our Roadway Network o Approx. 4 million miles paved roadways o Approx. 94% are surfaced with asphalt 32% of major roads are in poor condition o Incur costs of $67 billion a year $170 billion needed annually to improve o $85 billion for existing pavements 14

15 Pavement Roughness Defined in engineering practice as surface unevenness which adversely affects ride comfort Expressed by a numerical scale called the International Roughness Index (IRI) Quarter-car Model Inertial Profiler 15

Current Roughness Measurement Systems Inertial Profiler First developed by Elson Spangler and William Kelley Modern inertial profilometers require four basic sub-systems:

16 Current Roughness Measurement Systems Inertial Profiler First developed by Elson Spangler and William Kelley Modern inertial profilometers require four basic sub-systems: Accelerometers Height sensors Distance or a speed sensor Computer hardware and software International Cybernetics Corporation (ICC), Automatic Road Analyzer (ARAN), and K.J. Law profilometer 16

Limitations/Challenges of IRI Unable to provide distress information Multiple pavement sections can have the same IRI value Data collected at

17 Limitations/Challenges of IRI Unable to provide distress information Multiple pavement sections can have the same IRI value Data collected at low speeds can generate false peaks in the profile Create false spikes in the IRI parameter Accelerometer sensitivity affects pavement roughness 17

18 Motivation VDOT reported a contractor is employed to gather roughness data at an annual cost of $1.8 million Data are collected once-every-five years for secondary roads For small transportation agencies such counties and cities with low operating budgets, pavement condition data collection frequency may be limited Thus, M&R decisions are often performed using outdated data Infrequent roughness measurements also preclude the identification of rapidly developing distress features on pavements, such as potholes There is a need for a pavement roughness data collection system which is: Economical and simple Easily accessible Crowd-source based, having the potential to save agencies millions of dollars Providing data for more intelligent route selection 18

19 Research Objectives Development of a smartphone application, Roughness Capture, to measure pavement roughness Initial validation of IRI predictions using Roughness Capture, comparing cell-phone based IRI values to those obtained using an industry-standard inertial profiler 19

Hypothesis: Vertical Acceleration of Vehicle Pavement surface irregularities causes the vehicle wheels to move up and down with respect to the road surface, causing the vehicle cab to accelerate

20 Hypothesis: Vertical Acceleration of Vehicle Pavement surface irregularities causes the vehicle wheels to move up and down with respect to the road surface, causing the vehicle cab to accelerate (although cab movement is dampened by suspension) Roughness Capture has been used to collect vertical acceleration data in the vehicle cab - It is hypothesized that vehicle cab acceleration measured with smart phones can be combined with vehicle dynamics models to arrive at accurate measures of pavement IRI 20

21 Double Integration vs. Inverse State-Space Model Double Integration Scheme Acceleration Data ʃʃ Vehicle Cab (Dashboard) Displacement Profile, y IRI Roughness Capture Quarter-Car Model (ASTM) Inverse State-Space Modeling Scheme Pavement Profile, y IRI Roughness Capture State Space Modeling of Suspension for Vehicle Actually Used, Solved Inversely Quarter-Car Model (ASTM) 21

22 State-Space Model Output Input Zs is known, y is unknown Therefore, it is inverse problem 22

23 Data Collection and Site Location Three test sites : County Highway 32, 9, and 23 Test sites were selected with wide variety of distresses County Highway 32 is with very low or no distresses, and County Highway 23 is very rough pavement 23

24 IRI Estimation using ProVAL and MATLAB Script 250 County Highway MATLAB Script Esitimated IRI IRI (inch/mile) Unity Line ProVAL Estimated IRI, (inch/mile) 24

25 Inverse State Space: County Highway Smartphone Measured IRI (inch/mile) Honda CRV Profiler Measured IRI (inch/mile) 25

26 Inverse State Space: County Highway Smartphone Measured IRI (inch/mile) Honda CRV Profiler Measured IRI (inch/mile) 26

27 Inverse State Space: County Highway 23 Smartphone Measured IRI (inch/mile) Honda CRV 20 inch/mile offset line from unity line 10 inch/mile offset line from unity line Profiler Measured IRI (inch/mile) 27

28 Validation: Profiler vs. App Measured IRI Average Inertial Profiler IRI over Total Length (in/mi) Average Smartphone Measured IRI over Total Length (in/mi) 150 IRI (in/mi) IL-10E County Rd N 1200E County Rd 2500N County Rd 1000N County Road 9 County Rd 200E County Rd 800E County Rd 900E US-150 US-45 SB County Rd 2700N/300E US-136 IL-47 28

29 Effect of Different Vehicles on IRI Mazda 3 Honda CR-V Dodge Avenger Chevrolet Impala M1 (kg) M2 (kg) C1 (N*s/m) 1,500 1,400 1,550 1,500 K1 (N/m) 13,500 11,000 12,000 10,000 K2 (N/m) 200, , , ,000 29

30 IRI vs. Vehicle at County Highway Profiler IRI Mazda 3 Honda CRV Chevrolet Impala Dodge Avenger IRI (inch/mile) Very Poor Poor Fair Good Very Good Distance (mile) 30

31 Average IRI with Vehicles at County Highway 9 Profiler IRI Average Smartphone IRI 220 IRI (inch/mile) Very Poor Poor Fair Good Very Good Distance (mile) 31

Faulting (4) % pavements on the NHS in Poor condition Surface Type All Pavements IRI (inch/mile) Rating < 95 Good Area 95-170 Fair Areas with a

32 Moving Ahead For Progress (MAP-21) Performance Measure Data (1) % pavements on the Interstate Systems in Good condition 1. IRI (2) % pavements on the Interstate Systems in Poor condition 2. % Cracking 3. Rutting (3) % pavements on the NHS in Good condition 4. Faulting (4) % pavements on the NHS in Poor condition Surface Type All Pavements IRI (inch/mile) Rating < 95 Good Area Fair Areas with a population <1 million Fair Urbanized areas with population 1 million > 170 Poor Areas with a population <1 million > 220 Poor Urbanized areas with population 1 million 32

33 MAP-21 IRI Data: County Highway 9 Poor Profiler IRI Smartphone IRI IRI (inch/mile) Fair Good Distance (mile) 33

IRI values though distress types and locations are different Detection of

34 Surface Irregularity Detection IRI itself does not offer any idea of presence of surface irregularities Two different pavement profiles can generate same IRI values though distress types and locations are different Detection of surface irregularities location will give an idea of the severity of distresses 34

35 Localized Distress Detection 70 Number of Localized Distresses Distresses Detected by Pavement Engineer Distresses detected by Smartphone App 0 County Rd 2500N County Rd 900E US-136 US-136 IL-47 US-150 County Rd 2700N/300E County Rd N 1200E IL-10E County Rd 200E County Rd 1000N County Rd 800E 35

36 Bump/Pothole Locations 36

agencies Incorporation of pavement roughness values in the

37 Integration of IRI Data into Roadway Network Map Visualization of network condition is an outmost interest to transportation agencies Incorporation of pavement roughness values in the roadway Existing roadway network data Provide a link between PMS and GIS 37

38 IRI on Roadway Map using ArcGIS Very Good Good Fair Poor Very Poor 38

39 SOLVING PROBLEMS OF GLOBAL IMPORTANCE Applied Research Associates, Inc. 39

40 Acknowledgements NexTrans: USDOT Region V Regional University Transportation Center Applied Research Associates, Inc. 40

Ride Smoothness Measurement and Specification Issues. Nicholas Vitillo, Ph. D. Manager, Bureau of Research New Jersey Department of Transportation

Ride Smoothness Measurement and Specification Issues Nicholas Vitillo, Ph. D. Manager, Bureau of Research New Jersey Department of Transportation Components of Pavement Smoothness Surface Tolerance deviations